PROTEIN_EXPRESSION_ANALYSIS_FOR_DOWN'S_SYNDROME.ipynb

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    "colab": {
      "name": "PROTEIN EXPRESSION ANALYSIS FOR DOWN'S SYNDROME.ipynb",
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      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0oUqIc65sKD"
      },
      "source": [
        "## **IMPORTING LIBRARIES AND LOADING DATASET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-qUsTCz5QgJ"
      },
      "source": [
        "from sklearn import feature_selection\n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn as sk\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn import model_selection\n",
        "from sklearn.metrics import confusion_matrix,classification_report,roc_curve,f1_score,roc_auc_score,auc\n",
        "from sklearn. preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.feature_selection import SelectKBest,f_classif\n",
        "import seaborn as sns\n",
        "from scipy import interp\n",
        "from itertools import cycle\n",
        "\n",
        "mice=pd.read_csv(\"Data_Cortex_Nuclear.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IbZ9Wsd855E2"
      },
      "source": [
        "**THE DATASET CONSISTS OF 1080 ROWS AND 82 COLUMNS **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfjOL-VO555J",
        "outputId": "b14f91fd-45d0-45ed-8b8e-0669c7b411f3"
      },
      "source": [
        "mice.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1080, 82)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SKYGPpD06ePk"
      },
      "source": [
        "**OVERVIEW OF THE DATASET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "CMvsIw246evC",
        "outputId": "a97f2706-d777-4b5f-ee97-5b05a674a352"
      },
      "source": [
        "mice.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MouseID</th>\n",
              "      <th>DYRK1A_N</th>\n",
              "      <th>ITSN1_N</th>\n",
              "      <th>BDNF_N</th>\n",
              "      <th>NR1_N</th>\n",
              "      <th>NR2A_N</th>\n",
              "      <th>pAKT_N</th>\n",
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              "      <th>pCAMKII_N</th>\n",
              "      <th>pCREB_N</th>\n",
              "      <th>pELK_N</th>\n",
              "      <th>pERK_N</th>\n",
              "      <th>pJNK_N</th>\n",
              "      <th>PKCA_N</th>\n",
              "      <th>pMEK_N</th>\n",
              "      <th>pNR1_N</th>\n",
              "      <th>pNR2A_N</th>\n",
              "      <th>pNR2B_N</th>\n",
              "      <th>pPKCAB_N</th>\n",
              "      <th>pRSK_N</th>\n",
              "      <th>AKT_N</th>\n",
              "      <th>BRAF_N</th>\n",
              "      <th>CAMKII_N</th>\n",
              "      <th>CREB_N</th>\n",
              "      <th>ELK_N</th>\n",
              "      <th>ERK_N</th>\n",
              "      <th>GSK3B_N</th>\n",
              "      <th>JNK_N</th>\n",
              "      <th>MEK_N</th>\n",
              "      <th>TRKA_N</th>\n",
              "      <th>RSK_N</th>\n",
              "      <th>APP_N</th>\n",
              "      <th>Bcatenin_N</th>\n",
              "      <th>SOD1_N</th>\n",
              "      <th>MTOR_N</th>\n",
              "      <th>P38_N</th>\n",
              "      <th>pMTOR_N</th>\n",
              "      <th>DSCR1_N</th>\n",
              "      <th>AMPKA_N</th>\n",
              "      <th>NR2B_N</th>\n",
              "      <th>...</th>\n",
              "      <th>TIAM1_N</th>\n",
              "      <th>pP70S6_N</th>\n",
              "      <th>NUMB_N</th>\n",
              "      <th>P70S6_N</th>\n",
              "      <th>pGSK3B_N</th>\n",
              "      <th>pPKCG_N</th>\n",
              "      <th>CDK5_N</th>\n",
              "      <th>S6_N</th>\n",
              "      <th>ADARB1_N</th>\n",
              "      <th>AcetylH3K9_N</th>\n",
              "      <th>RRP1_N</th>\n",
              "      <th>BAX_N</th>\n",
              "      <th>ARC_N</th>\n",
              "      <th>ERBB4_N</th>\n",
              "      <th>nNOS_N</th>\n",
              "      <th>Tau_N</th>\n",
              "      <th>GFAP_N</th>\n",
              "      <th>GluR3_N</th>\n",
              "      <th>GluR4_N</th>\n",
              "      <th>IL1B_N</th>\n",
              "      <th>P3525_N</th>\n",
              "      <th>pCASP9_N</th>\n",
              "      <th>PSD95_N</th>\n",
              "      <th>SNCA_N</th>\n",
              "      <th>Ubiquitin_N</th>\n",
              "      <th>pGSK3B_Tyr216_N</th>\n",
              "      <th>SHH_N</th>\n",
              "      <th>BAD_N</th>\n",
              "      <th>BCL2_N</th>\n",
              "      <th>pS6_N</th>\n",
              "      <th>pCFOS_N</th>\n",
              "      <th>SYP_N</th>\n",
              "      <th>H3AcK18_N</th>\n",
              "      <th>EGR1_N</th>\n",
              "      <th>H3MeK4_N</th>\n",
              "      <th>CaNA_N</th>\n",
              "      <th>Genotype</th>\n",
              "      <th>Treatment</th>\n",
              "      <th>Behavior</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>309_1</td>\n",
              "      <td>0.503644</td>\n",
              "      <td>0.747193</td>\n",
              "      <td>0.430175</td>\n",
              "      <td>2.816329</td>\n",
              "      <td>5.990152</td>\n",
              "      <td>0.218830</td>\n",
              "      <td>0.177565</td>\n",
              "      <td>2.373744</td>\n",
              "      <td>0.232224</td>\n",
              "      <td>1.750936</td>\n",
              "      <td>0.687906</td>\n",
              "      <td>0.306382</td>\n",
              "      <td>0.402698</td>\n",
              "      <td>0.296927</td>\n",
              "      <td>1.022060</td>\n",
              "      <td>0.605673</td>\n",
              "      <td>1.877684</td>\n",
              "      <td>2.308745</td>\n",
              "      <td>0.441599</td>\n",
              "      <td>0.859366</td>\n",
              "      <td>0.416289</td>\n",
              "      <td>0.369608</td>\n",
              "      <td>0.178944</td>\n",
              "      <td>1.866358</td>\n",
              "      <td>3.685247</td>\n",
              "      <td>1.537227</td>\n",
              "      <td>0.264526</td>\n",
              "      <td>0.319677</td>\n",
              "      <td>0.813866</td>\n",
              "      <td>0.165846</td>\n",
              "      <td>0.453910</td>\n",
              "      <td>3.037621</td>\n",
              "      <td>0.369510</td>\n",
              "      <td>0.458539</td>\n",
              "      <td>0.335336</td>\n",
              "      <td>0.825192</td>\n",
              "      <td>0.576916</td>\n",
              "      <td>0.448099</td>\n",
              "      <td>0.586271</td>\n",
              "      <td>...</td>\n",
              "      <td>0.482864</td>\n",
              "      <td>0.294170</td>\n",
              "      <td>0.182150</td>\n",
              "      <td>0.842725</td>\n",
              "      <td>0.192608</td>\n",
              "      <td>1.443091</td>\n",
              "      <td>0.294700</td>\n",
              "      <td>0.354605</td>\n",
              "      <td>1.339070</td>\n",
              "      <td>0.170119</td>\n",
              "      <td>0.159102</td>\n",
              "      <td>0.188852</td>\n",
              "      <td>0.106305</td>\n",
              "      <td>0.144989</td>\n",
              "      <td>0.176668</td>\n",
              "      <td>0.125190</td>\n",
              "      <td>0.115291</td>\n",
              "      <td>0.228043</td>\n",
              "      <td>0.142756</td>\n",
              "      <td>0.430957</td>\n",
              "      <td>0.247538</td>\n",
              "      <td>1.603310</td>\n",
              "      <td>2.014875</td>\n",
              "      <td>0.108234</td>\n",
              "      <td>1.044979</td>\n",
              "      <td>0.831557</td>\n",
              "      <td>0.188852</td>\n",
              "      <td>0.122652</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.106305</td>\n",
              "      <td>0.108336</td>\n",
              "      <td>0.427099</td>\n",
              "      <td>0.114783</td>\n",
              "      <td>0.131790</td>\n",
              "      <td>0.128186</td>\n",
              "      <td>1.675652</td>\n",
              "      <td>Control</td>\n",
              "      <td>Memantine</td>\n",
              "      <td>C/S</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>309_2</td>\n",
              "      <td>0.514617</td>\n",
              "      <td>0.689064</td>\n",
              "      <td>0.411770</td>\n",
              "      <td>2.789514</td>\n",
              "      <td>5.685038</td>\n",
              "      <td>0.211636</td>\n",
              "      <td>0.172817</td>\n",
              "      <td>2.292150</td>\n",
              "      <td>0.226972</td>\n",
              "      <td>1.596377</td>\n",
              "      <td>0.695006</td>\n",
              "      <td>0.299051</td>\n",
              "      <td>0.385987</td>\n",
              "      <td>0.281319</td>\n",
              "      <td>0.956676</td>\n",
              "      <td>0.587559</td>\n",
              "      <td>1.725774</td>\n",
              "      <td>2.043037</td>\n",
              "      <td>0.445222</td>\n",
              "      <td>0.834659</td>\n",
              "      <td>0.400364</td>\n",
              "      <td>0.356178</td>\n",
              "      <td>0.173680</td>\n",
              "      <td>1.761047</td>\n",
              "      <td>3.485287</td>\n",
              "      <td>1.509249</td>\n",
              "      <td>0.255727</td>\n",
              "      <td>0.304419</td>\n",
              "      <td>0.780504</td>\n",
              "      <td>0.157194</td>\n",
              "      <td>0.430940</td>\n",
              "      <td>2.921882</td>\n",
              "      <td>0.342279</td>\n",
              "      <td>0.423560</td>\n",
              "      <td>0.324835</td>\n",
              "      <td>0.761718</td>\n",
              "      <td>0.545097</td>\n",
              "      <td>0.420876</td>\n",
              "      <td>0.545097</td>\n",
              "      <td>...</td>\n",
              "      <td>0.454519</td>\n",
              "      <td>0.276431</td>\n",
              "      <td>0.182086</td>\n",
              "      <td>0.847615</td>\n",
              "      <td>0.194815</td>\n",
              "      <td>1.439460</td>\n",
              "      <td>0.294060</td>\n",
              "      <td>0.354548</td>\n",
              "      <td>1.306323</td>\n",
              "      <td>0.171427</td>\n",
              "      <td>0.158129</td>\n",
              "      <td>0.184570</td>\n",
              "      <td>0.106592</td>\n",
              "      <td>0.150471</td>\n",
              "      <td>0.178309</td>\n",
              "      <td>0.134275</td>\n",
              "      <td>0.118235</td>\n",
              "      <td>0.238073</td>\n",
              "      <td>0.142037</td>\n",
              "      <td>0.457156</td>\n",
              "      <td>0.257632</td>\n",
              "      <td>1.671738</td>\n",
              "      <td>2.004605</td>\n",
              "      <td>0.109749</td>\n",
              "      <td>1.009883</td>\n",
              "      <td>0.849270</td>\n",
              "      <td>0.200404</td>\n",
              "      <td>0.116682</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.106592</td>\n",
              "      <td>0.104315</td>\n",
              "      <td>0.441581</td>\n",
              "      <td>0.111974</td>\n",
              "      <td>0.135103</td>\n",
              "      <td>0.131119</td>\n",
              "      <td>1.743610</td>\n",
              "      <td>Control</td>\n",
              "      <td>Memantine</td>\n",
              "      <td>C/S</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>309_3</td>\n",
              "      <td>0.509183</td>\n",
              "      <td>0.730247</td>\n",
              "      <td>0.418309</td>\n",
              "      <td>2.687201</td>\n",
              "      <td>5.622059</td>\n",
              "      <td>0.209011</td>\n",
              "      <td>0.175722</td>\n",
              "      <td>2.283337</td>\n",
              "      <td>0.230247</td>\n",
              "      <td>1.561316</td>\n",
              "      <td>0.677348</td>\n",
              "      <td>0.291276</td>\n",
              "      <td>0.381002</td>\n",
              "      <td>0.281710</td>\n",
              "      <td>1.003635</td>\n",
              "      <td>0.602449</td>\n",
              "      <td>1.731873</td>\n",
              "      <td>2.017984</td>\n",
              "      <td>0.467668</td>\n",
              "      <td>0.814329</td>\n",
              "      <td>0.399847</td>\n",
              "      <td>0.368089</td>\n",
              "      <td>0.173905</td>\n",
              "      <td>1.765544</td>\n",
              "      <td>3.571456</td>\n",
              "      <td>1.501244</td>\n",
              "      <td>0.259614</td>\n",
              "      <td>0.311747</td>\n",
              "      <td>0.785154</td>\n",
              "      <td>0.160895</td>\n",
              "      <td>0.423187</td>\n",
              "      <td>2.944136</td>\n",
              "      <td>0.343696</td>\n",
              "      <td>0.425005</td>\n",
              "      <td>0.324852</td>\n",
              "      <td>0.757031</td>\n",
              "      <td>0.543620</td>\n",
              "      <td>0.404630</td>\n",
              "      <td>0.552994</td>\n",
              "      <td>...</td>\n",
              "      <td>0.447197</td>\n",
              "      <td>0.256648</td>\n",
              "      <td>0.184388</td>\n",
              "      <td>0.856166</td>\n",
              "      <td>0.200737</td>\n",
              "      <td>1.524364</td>\n",
              "      <td>0.301881</td>\n",
              "      <td>0.386087</td>\n",
              "      <td>1.279600</td>\n",
              "      <td>0.185456</td>\n",
              "      <td>0.148696</td>\n",
              "      <td>0.190532</td>\n",
              "      <td>0.108303</td>\n",
              "      <td>0.145330</td>\n",
              "      <td>0.176213</td>\n",
              "      <td>0.132560</td>\n",
              "      <td>0.117760</td>\n",
              "      <td>0.244817</td>\n",
              "      <td>0.142445</td>\n",
              "      <td>0.510472</td>\n",
              "      <td>0.255343</td>\n",
              "      <td>1.663550</td>\n",
              "      <td>2.016831</td>\n",
              "      <td>0.108196</td>\n",
              "      <td>0.996848</td>\n",
              "      <td>0.846709</td>\n",
              "      <td>0.193685</td>\n",
              "      <td>0.118508</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.108303</td>\n",
              "      <td>0.106219</td>\n",
              "      <td>0.435777</td>\n",
              "      <td>0.111883</td>\n",
              "      <td>0.133362</td>\n",
              "      <td>0.127431</td>\n",
              "      <td>1.926427</td>\n",
              "      <td>Control</td>\n",
              "      <td>Memantine</td>\n",
              "      <td>C/S</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>309_4</td>\n",
              "      <td>0.442107</td>\n",
              "      <td>0.617076</td>\n",
              "      <td>0.358626</td>\n",
              "      <td>2.466947</td>\n",
              "      <td>4.979503</td>\n",
              "      <td>0.222886</td>\n",
              "      <td>0.176463</td>\n",
              "      <td>2.152301</td>\n",
              "      <td>0.207004</td>\n",
              "      <td>1.595086</td>\n",
              "      <td>0.583277</td>\n",
              "      <td>0.296729</td>\n",
              "      <td>0.377087</td>\n",
              "      <td>0.313832</td>\n",
              "      <td>0.875390</td>\n",
              "      <td>0.520293</td>\n",
              "      <td>1.566852</td>\n",
              "      <td>2.132754</td>\n",
              "      <td>0.477671</td>\n",
              "      <td>0.727705</td>\n",
              "      <td>0.385639</td>\n",
              "      <td>0.362970</td>\n",
              "      <td>0.179449</td>\n",
              "      <td>1.286277</td>\n",
              "      <td>2.970137</td>\n",
              "      <td>1.419710</td>\n",
              "      <td>0.259536</td>\n",
              "      <td>0.279218</td>\n",
              "      <td>0.734492</td>\n",
              "      <td>0.162210</td>\n",
              "      <td>0.410615</td>\n",
              "      <td>2.500204</td>\n",
              "      <td>0.344509</td>\n",
              "      <td>0.429211</td>\n",
              "      <td>0.330121</td>\n",
              "      <td>0.746980</td>\n",
              "      <td>0.546763</td>\n",
              "      <td>0.386860</td>\n",
              "      <td>0.547849</td>\n",
              "      <td>...</td>\n",
              "      <td>0.442650</td>\n",
              "      <td>0.398534</td>\n",
              "      <td>0.161768</td>\n",
              "      <td>0.760234</td>\n",
              "      <td>0.184169</td>\n",
              "      <td>1.612382</td>\n",
              "      <td>0.296382</td>\n",
              "      <td>0.290680</td>\n",
              "      <td>1.198765</td>\n",
              "      <td>0.159799</td>\n",
              "      <td>0.166112</td>\n",
              "      <td>0.185323</td>\n",
              "      <td>0.103184</td>\n",
              "      <td>0.140656</td>\n",
              "      <td>0.163804</td>\n",
              "      <td>0.123210</td>\n",
              "      <td>0.117439</td>\n",
              "      <td>0.234947</td>\n",
              "      <td>0.145068</td>\n",
              "      <td>0.430996</td>\n",
              "      <td>0.251103</td>\n",
              "      <td>1.484624</td>\n",
              "      <td>1.957233</td>\n",
              "      <td>0.119883</td>\n",
              "      <td>0.990225</td>\n",
              "      <td>0.833277</td>\n",
              "      <td>0.192112</td>\n",
              "      <td>0.132781</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.103184</td>\n",
              "      <td>0.111262</td>\n",
              "      <td>0.391691</td>\n",
              "      <td>0.130405</td>\n",
              "      <td>0.147444</td>\n",
              "      <td>0.146901</td>\n",
              "      <td>1.700563</td>\n",
              "      <td>Control</td>\n",
              "      <td>Memantine</td>\n",
              "      <td>C/S</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>309_5</td>\n",
              "      <td>0.434940</td>\n",
              "      <td>0.617430</td>\n",
              "      <td>0.358802</td>\n",
              "      <td>2.365785</td>\n",
              "      <td>4.718679</td>\n",
              "      <td>0.213106</td>\n",
              "      <td>0.173627</td>\n",
              "      <td>2.134014</td>\n",
              "      <td>0.192158</td>\n",
              "      <td>1.504230</td>\n",
              "      <td>0.550960</td>\n",
              "      <td>0.286961</td>\n",
              "      <td>0.363502</td>\n",
              "      <td>0.277964</td>\n",
              "      <td>0.864912</td>\n",
              "      <td>0.507990</td>\n",
              "      <td>1.480059</td>\n",
              "      <td>2.013697</td>\n",
              "      <td>0.483416</td>\n",
              "      <td>0.687794</td>\n",
              "      <td>0.367531</td>\n",
              "      <td>0.355311</td>\n",
              "      <td>0.174836</td>\n",
              "      <td>1.324695</td>\n",
              "      <td>2.896334</td>\n",
              "      <td>1.359876</td>\n",
              "      <td>0.250705</td>\n",
              "      <td>0.273667</td>\n",
              "      <td>0.702699</td>\n",
              "      <td>0.154827</td>\n",
              "      <td>0.398550</td>\n",
              "      <td>2.456560</td>\n",
              "      <td>0.329126</td>\n",
              "      <td>0.408755</td>\n",
              "      <td>0.313415</td>\n",
              "      <td>0.691956</td>\n",
              "      <td>0.536860</td>\n",
              "      <td>0.360816</td>\n",
              "      <td>0.512824</td>\n",
              "      <td>...</td>\n",
              "      <td>0.419095</td>\n",
              "      <td>0.393447</td>\n",
              "      <td>0.160200</td>\n",
              "      <td>0.768113</td>\n",
              "      <td>0.185718</td>\n",
              "      <td>1.645807</td>\n",
              "      <td>0.296829</td>\n",
              "      <td>0.309345</td>\n",
              "      <td>1.206995</td>\n",
              "      <td>0.164650</td>\n",
              "      <td>0.160687</td>\n",
              "      <td>0.188221</td>\n",
              "      <td>0.104784</td>\n",
              "      <td>0.141983</td>\n",
              "      <td>0.167710</td>\n",
              "      <td>0.136838</td>\n",
              "      <td>0.116048</td>\n",
              "      <td>0.255528</td>\n",
              "      <td>0.140871</td>\n",
              "      <td>0.481227</td>\n",
              "      <td>0.251773</td>\n",
              "      <td>1.534835</td>\n",
              "      <td>2.009109</td>\n",
              "      <td>0.119524</td>\n",
              "      <td>0.997775</td>\n",
              "      <td>0.878668</td>\n",
              "      <td>0.205604</td>\n",
              "      <td>0.129954</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.104784</td>\n",
              "      <td>0.110694</td>\n",
              "      <td>0.434154</td>\n",
              "      <td>0.118481</td>\n",
              "      <td>0.140314</td>\n",
              "      <td>0.148380</td>\n",
              "      <td>1.839730</td>\n",
              "      <td>Control</td>\n",
              "      <td>Memantine</td>\n",
              "      <td>C/S</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>309_6</td>\n",
              "      <td>0.447506</td>\n",
              "      <td>0.628176</td>\n",
              "      <td>0.367388</td>\n",
              "      <td>2.385939</td>\n",
              "      <td>4.807635</td>\n",
              "      <td>0.218578</td>\n",
              "      <td>0.176233</td>\n",
              "      <td>2.141282</td>\n",
              "      <td>0.195188</td>\n",
              "      <td>1.442398</td>\n",
              "      <td>0.566340</td>\n",
              "      <td>0.289824</td>\n",
              "      <td>0.363893</td>\n",
              "      <td>0.266837</td>\n",
              "      <td>0.859121</td>\n",
              "      <td>0.521307</td>\n",
              "      <td>1.538244</td>\n",
              "      <td>1.968275</td>\n",
              "      <td>0.495900</td>\n",
              "      <td>0.672402</td>\n",
              "      <td>0.369404</td>\n",
              "      <td>0.357172</td>\n",
              "      <td>0.179728</td>\n",
              "      <td>1.227450</td>\n",
              "      <td>2.956983</td>\n",
              "      <td>1.447910</td>\n",
              "      <td>0.250840</td>\n",
              "      <td>0.284044</td>\n",
              "      <td>0.704396</td>\n",
              "      <td>0.156876</td>\n",
              "      <td>0.391047</td>\n",
              "      <td>2.467133</td>\n",
              "      <td>0.327598</td>\n",
              "      <td>0.404490</td>\n",
              "      <td>0.296276</td>\n",
              "      <td>0.674419</td>\n",
              "      <td>0.539723</td>\n",
              "      <td>0.354214</td>\n",
              "      <td>0.514316</td>\n",
              "      <td>...</td>\n",
              "      <td>0.412824</td>\n",
              "      <td>0.382578</td>\n",
              "      <td>0.162330</td>\n",
              "      <td>0.779695</td>\n",
              "      <td>0.186793</td>\n",
              "      <td>1.634615</td>\n",
              "      <td>0.288037</td>\n",
              "      <td>0.332367</td>\n",
              "      <td>1.123445</td>\n",
              "      <td>0.175693</td>\n",
              "      <td>0.150594</td>\n",
              "      <td>0.183824</td>\n",
              "      <td>0.106476</td>\n",
              "      <td>0.139564</td>\n",
              "      <td>0.174844</td>\n",
              "      <td>0.130515</td>\n",
              "      <td>0.115243</td>\n",
              "      <td>0.236850</td>\n",
              "      <td>0.136454</td>\n",
              "      <td>0.478577</td>\n",
              "      <td>0.244485</td>\n",
              "      <td>1.507777</td>\n",
              "      <td>2.003535</td>\n",
              "      <td>0.120687</td>\n",
              "      <td>0.920178</td>\n",
              "      <td>0.843679</td>\n",
              "      <td>0.190469</td>\n",
              "      <td>0.131575</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.106476</td>\n",
              "      <td>0.109446</td>\n",
              "      <td>0.439833</td>\n",
              "      <td>0.116657</td>\n",
              "      <td>0.140766</td>\n",
              "      <td>0.142180</td>\n",
              "      <td>1.816389</td>\n",
              "      <td>Control</td>\n",
              "      <td>Memantine</td>\n",
              "      <td>C/S</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>309_7</td>\n",
              "      <td>0.428033</td>\n",
              "      <td>0.573696</td>\n",
              "      <td>0.342709</td>\n",
              "      <td>2.334224</td>\n",
              "      <td>4.473130</td>\n",
              "      <td>0.225173</td>\n",
              "      <td>0.184004</td>\n",
              "      <td>2.012414</td>\n",
              "      <td>0.195789</td>\n",
              "      <td>1.612036</td>\n",
              "      <td>0.509899</td>\n",
              "      <td>0.299654</td>\n",
              "      <td>0.371150</td>\n",
              "      <td>0.277656</td>\n",
              "      <td>0.844280</td>\n",
              "      <td>0.485387</td>\n",
              "      <td>1.494815</td>\n",
              "      <td>2.012571</td>\n",
              "      <td>0.497486</td>\n",
              "      <td>0.646135</td>\n",
              "      <td>0.395977</td>\n",
              "      <td>0.359522</td>\n",
              "      <td>0.186047</td>\n",
              "      <td>1.107794</td>\n",
              "      <td>2.599623</td>\n",
              "      <td>1.364236</td>\n",
              "      <td>0.261471</td>\n",
              "      <td>0.276241</td>\n",
              "      <td>0.693118</td>\n",
              "      <td>0.162162</td>\n",
              "      <td>0.405563</td>\n",
              "      <td>2.288812</td>\n",
              "      <td>0.345380</td>\n",
              "      <td>0.427561</td>\n",
              "      <td>0.333909</td>\n",
              "      <td>0.757542</td>\n",
              "      <td>0.558611</td>\n",
              "      <td>0.368950</td>\n",
              "      <td>0.544626</td>\n",
              "      <td>...</td>\n",
              "      <td>0.420490</td>\n",
              "      <td>0.465116</td>\n",
              "      <td>0.145566</td>\n",
              "      <td>0.702248</td>\n",
              "      <td>0.177033</td>\n",
              "      <td>1.773846</td>\n",
              "      <td>0.292195</td>\n",
              "      <td>0.266857</td>\n",
              "      <td>1.032284</td>\n",
              "      <td>0.147855</td>\n",
              "      <td>0.149816</td>\n",
              "      <td>0.181610</td>\n",
              "      <td>0.097834</td>\n",
              "      <td>0.142787</td>\n",
              "      <td>0.151859</td>\n",
              "      <td>0.115979</td>\n",
              "      <td>0.114753</td>\n",
              "      <td>0.224520</td>\n",
              "      <td>0.144830</td>\n",
              "      <td>0.415529</td>\n",
              "      <td>0.238006</td>\n",
              "      <td>1.338782</td>\n",
              "      <td>1.861708</td>\n",
              "      <td>0.117041</td>\n",
              "      <td>1.028770</td>\n",
              "      <td>0.798284</td>\n",
              "      <td>0.181610</td>\n",
              "      <td>0.141806</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.097834</td>\n",
              "      <td>0.111483</td>\n",
              "      <td>0.406293</td>\n",
              "      <td>0.137229</td>\n",
              "      <td>0.156682</td>\n",
              "      <td>0.157499</td>\n",
              "      <td>1.528484</td>\n",
              "      <td>Control</td>\n",
              "      <td>Memantine</td>\n",
              "      <td>C/S</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>309_8</td>\n",
              "      <td>0.416923</td>\n",
              "      <td>0.564036</td>\n",
              "      <td>0.327703</td>\n",
              "      <td>2.260135</td>\n",
              "      <td>4.268735</td>\n",
              "      <td>0.214834</td>\n",
              "      <td>0.179668</td>\n",
              "      <td>2.007985</td>\n",
              "      <td>0.189803</td>\n",
              "      <td>1.424601</td>\n",
              "      <td>0.501075</td>\n",
              "      <td>0.283016</td>\n",
              "      <td>0.358876</td>\n",
              "      <td>0.256450</td>\n",
              "      <td>0.818643</td>\n",
              "      <td>0.478501</td>\n",
              "      <td>1.409398</td>\n",
              "      <td>1.857033</td>\n",
              "      <td>0.489558</td>\n",
              "      <td>0.628071</td>\n",
              "      <td>0.360719</td>\n",
              "      <td>0.360719</td>\n",
              "      <td>0.171837</td>\n",
              "      <td>1.077396</td>\n",
              "      <td>2.550215</td>\n",
              "      <td>1.316646</td>\n",
              "      <td>0.250614</td>\n",
              "      <td>0.265356</td>\n",
              "      <td>0.676597</td>\n",
              "      <td>0.159552</td>\n",
              "      <td>0.375461</td>\n",
              "      <td>2.281173</td>\n",
              "      <td>0.328163</td>\n",
              "      <td>0.407248</td>\n",
              "      <td>0.322789</td>\n",
              "      <td>0.695178</td>\n",
              "      <td>0.525031</td>\n",
              "      <td>0.340141</td>\n",
              "      <td>0.509828</td>\n",
              "      <td>...</td>\n",
              "      <td>0.392967</td>\n",
              "      <td>0.440418</td>\n",
              "      <td>0.147848</td>\n",
              "      <td>0.681941</td>\n",
              "      <td>0.174599</td>\n",
              "      <td>1.795781</td>\n",
              "      <td>0.288523</td>\n",
              "      <td>0.288608</td>\n",
              "      <td>1.152068</td>\n",
              "      <td>0.152405</td>\n",
              "      <td>0.154515</td>\n",
              "      <td>0.182869</td>\n",
              "      <td>0.099494</td>\n",
              "      <td>0.140169</td>\n",
              "      <td>0.158734</td>\n",
              "      <td>0.119578</td>\n",
              "      <td>0.116203</td>\n",
              "      <td>0.246835</td>\n",
              "      <td>0.134430</td>\n",
              "      <td>0.451139</td>\n",
              "      <td>0.241435</td>\n",
              "      <td>1.386076</td>\n",
              "      <td>1.969451</td>\n",
              "      <td>0.116878</td>\n",
              "      <td>0.943544</td>\n",
              "      <td>0.843966</td>\n",
              "      <td>0.191561</td>\n",
              "      <td>0.141013</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.099494</td>\n",
              "      <td>0.113080</td>\n",
              "      <td>0.382532</td>\n",
              "      <td>0.123376</td>\n",
              "      <td>0.155612</td>\n",
              "      <td>0.159409</td>\n",
              "      <td>1.652658</td>\n",
              "      <td>Control</td>\n",
              "      <td>Memantine</td>\n",
              "      <td>C/S</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>309_9</td>\n",
              "      <td>0.386311</td>\n",
              "      <td>0.538428</td>\n",
              "      <td>0.317720</td>\n",
              "      <td>2.125725</td>\n",
              "      <td>4.063950</td>\n",
              "      <td>0.207222</td>\n",
              "      <td>0.167778</td>\n",
              "      <td>1.861514</td>\n",
              "      <td>0.180684</td>\n",
              "      <td>1.261891</td>\n",
              "      <td>0.476653</td>\n",
              "      <td>0.266966</td>\n",
              "      <td>0.337007</td>\n",
              "      <td>0.242604</td>\n",
              "      <td>0.748260</td>\n",
              "      <td>0.458962</td>\n",
              "      <td>1.341792</td>\n",
              "      <td>1.822071</td>\n",
              "      <td>0.464472</td>\n",
              "      <td>0.600638</td>\n",
              "      <td>0.326566</td>\n",
              "      <td>0.328886</td>\n",
              "      <td>0.170389</td>\n",
              "      <td>1.008846</td>\n",
              "      <td>2.434600</td>\n",
              "      <td>1.252030</td>\n",
              "      <td>0.230423</td>\n",
              "      <td>0.252610</td>\n",
              "      <td>0.635586</td>\n",
              "      <td>0.154147</td>\n",
              "      <td>0.351218</td>\n",
              "      <td>2.139211</td>\n",
              "      <td>0.309165</td>\n",
              "      <td>0.378480</td>\n",
              "      <td>0.294519</td>\n",
              "      <td>0.636891</td>\n",
              "      <td>0.484919</td>\n",
              "      <td>0.323376</td>\n",
              "      <td>0.479988</td>\n",
              "      <td>...</td>\n",
              "      <td>0.357599</td>\n",
              "      <td>0.421694</td>\n",
              "      <td>0.150121</td>\n",
              "      <td>0.710808</td>\n",
              "      <td>0.167472</td>\n",
              "      <td>1.805249</td>\n",
              "      <td>0.294458</td>\n",
              "      <td>0.294113</td>\n",
              "      <td>1.107907</td>\n",
              "      <td>0.158840</td>\n",
              "      <td>0.148653</td>\n",
              "      <td>0.178522</td>\n",
              "      <td>0.105145</td>\n",
              "      <td>0.137776</td>\n",
              "      <td>0.160394</td>\n",
              "      <td>0.123964</td>\n",
              "      <td>0.116799</td>\n",
              "      <td>0.233857</td>\n",
              "      <td>0.132597</td>\n",
              "      <td>0.455974</td>\n",
              "      <td>0.236015</td>\n",
              "      <td>1.404178</td>\n",
              "      <td>1.958736</td>\n",
              "      <td>0.113864</td>\n",
              "      <td>0.946910</td>\n",
              "      <td>0.832182</td>\n",
              "      <td>0.186378</td>\n",
              "      <td>0.139589</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.105145</td>\n",
              "      <td>0.105490</td>\n",
              "      <td>0.435687</td>\n",
              "      <td>0.125173</td>\n",
              "      <td>0.152711</td>\n",
              "      <td>0.158235</td>\n",
              "      <td>1.834599</td>\n",
              "      <td>Control</td>\n",
              "      <td>Memantine</td>\n",
              "      <td>C/S</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>309_10</td>\n",
              "      <td>0.380827</td>\n",
              "      <td>0.499294</td>\n",
              "      <td>0.362462</td>\n",
              "      <td>2.096266</td>\n",
              "      <td>3.598587</td>\n",
              "      <td>0.227649</td>\n",
              "      <td>0.188093</td>\n",
              "      <td>1.717861</td>\n",
              "      <td>0.188093</td>\n",
              "      <td>1.414329</td>\n",
              "      <td>0.455499</td>\n",
              "      <td>0.324117</td>\n",
              "      <td>0.368315</td>\n",
              "      <td>0.283754</td>\n",
              "      <td>0.743491</td>\n",
              "      <td>0.444198</td>\n",
              "      <td>1.355399</td>\n",
              "      <td>1.783249</td>\n",
              "      <td>0.493037</td>\n",
              "      <td>0.570535</td>\n",
              "      <td>0.335419</td>\n",
              "      <td>0.392129</td>\n",
              "      <td>0.198385</td>\n",
              "      <td>0.906155</td>\n",
              "      <td>2.169929</td>\n",
              "      <td>1.248234</td>\n",
              "      <td>0.257719</td>\n",
              "      <td>0.301514</td>\n",
              "      <td>0.636731</td>\n",
              "      <td>0.174369</td>\n",
              "      <td>0.380424</td>\n",
              "      <td>1.922906</td>\n",
              "      <td>0.373966</td>\n",
              "      <td>0.433502</td>\n",
              "      <td>0.394349</td>\n",
              "      <td>0.742079</td>\n",
              "      <td>0.553179</td>\n",
              "      <td>0.357215</td>\n",
              "      <td>0.530979</td>\n",
              "      <td>...</td>\n",
              "      <td>0.426034</td>\n",
              "      <td>0.505752</td>\n",
              "      <td>0.140063</td>\n",
              "      <td>0.637927</td>\n",
              "      <td>0.175585</td>\n",
              "      <td>1.926596</td>\n",
              "      <td>0.301821</td>\n",
              "      <td>0.249775</td>\n",
              "      <td>0.930980</td>\n",
              "      <td>0.145459</td>\n",
              "      <td>0.166254</td>\n",
              "      <td>0.171088</td>\n",
              "      <td>0.093300</td>\n",
              "      <td>0.124438</td>\n",
              "      <td>0.150629</td>\n",
              "      <td>0.117581</td>\n",
              "      <td>0.126574</td>\n",
              "      <td>0.224146</td>\n",
              "      <td>0.148156</td>\n",
              "      <td>0.410971</td>\n",
              "      <td>0.244829</td>\n",
              "      <td>1.288669</td>\n",
              "      <td>1.818907</td>\n",
              "      <td>0.139951</td>\n",
              "      <td>0.970324</td>\n",
              "      <td>0.803395</td>\n",
              "      <td>0.189523</td>\n",
              "      <td>0.183678</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.093300</td>\n",
              "      <td>0.116906</td>\n",
              "      <td>0.414793</td>\n",
              "      <td>0.146133</td>\n",
              "      <td>0.188849</td>\n",
              "      <td>0.192334</td>\n",
              "      <td>1.516862</td>\n",
              "      <td>Control</td>\n",
              "      <td>Memantine</td>\n",
              "      <td>C/S</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 82 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  MouseID  DYRK1A_N   ITSN1_N    BDNF_N  ...  Genotype  Treatment  Behavior   class\n",
              "0   309_1  0.503644  0.747193  0.430175  ...   Control  Memantine       C/S  c-CS-m\n",
              "1   309_2  0.514617  0.689064  0.411770  ...   Control  Memantine       C/S  c-CS-m\n",
              "2   309_3  0.509183  0.730247  0.418309  ...   Control  Memantine       C/S  c-CS-m\n",
              "3   309_4  0.442107  0.617076  0.358626  ...   Control  Memantine       C/S  c-CS-m\n",
              "4   309_5  0.434940  0.617430  0.358802  ...   Control  Memantine       C/S  c-CS-m\n",
              "5   309_6  0.447506  0.628176  0.367388  ...   Control  Memantine       C/S  c-CS-m\n",
              "6   309_7  0.428033  0.573696  0.342709  ...   Control  Memantine       C/S  c-CS-m\n",
              "7   309_8  0.416923  0.564036  0.327703  ...   Control  Memantine       C/S  c-CS-m\n",
              "8   309_9  0.386311  0.538428  0.317720  ...   Control  Memantine       C/S  c-CS-m\n",
              "9  309_10  0.380827  0.499294  0.362462  ...   Control  Memantine       C/S  c-CS-m\n",
              "\n",
              "[10 rows x 82 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCujx-Nn7GRP"
      },
      "source": [
        "**VISUALIZING NULL VALUES IN THE DATASET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "oI1LDC2i7Fxc",
        "outputId": "c8e53657-af6f-405b-a1a9-2c87fd2fcd59"
      },
      "source": [
        "sns.heatmap(mice.isnull())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe24f2c3850>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DHj7iQgc7Ffv"
      },
      "source": [
        "**CHECKING FOR NULL VALUES AND FILLING THEM WITH THE MEAN VALUES **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XMZk-upQ7jjW",
        "outputId": "56608674-83f3-4c6d-88c4-7af857f2f22c"
      },
      "source": [
        "mice.isnull().sum(axis=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       1\n",
              "1       1\n",
              "2       1\n",
              "3       1\n",
              "4       1\n",
              "       ..\n",
              "1075    1\n",
              "1076    1\n",
              "1077    1\n",
              "1078    1\n",
              "1079    1\n",
              "Length: 1080, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ibx4gKbK70mB",
        "outputId": "c7bb5e23-d381-49de-93f8-97dd14ff0728"
      },
      "source": [
        "nmice = mice.fillna(mice.mean())\n",
        "\n",
        "nmice.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MouseID      0\n",
              "DYRK1A_N     0\n",
              "ITSN1_N      0\n",
              "BDNF_N       0\n",
              "NR1_N        0\n",
              "            ..\n",
              "CaNA_N       0\n",
              "Genotype     0\n",
              "Treatment    0\n",
              "Behavior     0\n",
              "class        0\n",
              "Length: 82, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KM6-C23s8Etw"
      },
      "source": [
        "**IDENTIFYING CATEGORICAL AND NUMERICAL ATTRIBUTES**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JyX7aIua8H6y",
        "outputId": "ed89054f-c63b-4aa5-b3b1-1fefdaea4b8a"
      },
      "source": [
        "def value_type(mice):\n",
        "  categorical=[]\n",
        "  numerical=[]\n",
        "  for i in mice.columns:\n",
        "    if mice[i].dtype == 'object':\n",
        "      categorical.append(i)\n",
        "    else:\n",
        "      numerical.append(i)\n",
        "  return categorical,numerical\n",
        "\n",
        "category,numerical=value_type(mice)\n",
        "print('columns with categorical values:',category)\n",
        "print('columns with numerical values:\\n',numerical)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "columns with categorical values: ['MouseID', 'Genotype', 'Treatment', 'Behavior', 'class']\n",
            "columns with numerical values:\n",
            " ['DYRK1A_N', 'ITSN1_N', 'BDNF_N', 'NR1_N', 'NR2A_N', 'pAKT_N', 'pBRAF_N', 'pCAMKII_N', 'pCREB_N', 'pELK_N', 'pERK_N', 'pJNK_N', 'PKCA_N', 'pMEK_N', 'pNR1_N', 'pNR2A_N', 'pNR2B_N', 'pPKCAB_N', 'pRSK_N', 'AKT_N', 'BRAF_N', 'CAMKII_N', 'CREB_N', 'ELK_N', 'ERK_N', 'GSK3B_N', 'JNK_N', 'MEK_N', 'TRKA_N', 'RSK_N', 'APP_N', 'Bcatenin_N', 'SOD1_N', 'MTOR_N', 'P38_N', 'pMTOR_N', 'DSCR1_N', 'AMPKA_N', 'NR2B_N', 'pNUMB_N', 'RAPTOR_N', 'TIAM1_N', 'pP70S6_N', 'NUMB_N', 'P70S6_N', 'pGSK3B_N', 'pPKCG_N', 'CDK5_N', 'S6_N', 'ADARB1_N', 'AcetylH3K9_N', 'RRP1_N', 'BAX_N', 'ARC_N', 'ERBB4_N', 'nNOS_N', 'Tau_N', 'GFAP_N', 'GluR3_N', 'GluR4_N', 'IL1B_N', 'P3525_N', 'pCASP9_N', 'PSD95_N', 'SNCA_N', 'Ubiquitin_N', 'pGSK3B_Tyr216_N', 'SHH_N', 'BAD_N', 'BCL2_N', 'pS6_N', 'pCFOS_N', 'SYP_N', 'H3AcK18_N', 'EGR1_N', 'H3MeK4_N', 'CaNA_N']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvTJppyE8b1H"
      },
      "source": [
        "**IDENTIFICATION OF VALUES IN THE CATEGORICAL COLUMNS**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CIl1eaRE9TPi"
      },
      "source": [
        "**CONTROL--NORMAL MICE**\n",
        "\n",
        "**TS65DN--TRISOMIC OR DOWN SYNDROME AFFECTED MICE **\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RgZ3FQ2q8VvF",
        "outputId": "75ff34f0-2e80-4c8a-bc0b-860685eff49c"
      },
      "source": [
        "print('values in Genotype column:',mice['Genotype'].nunique())\n",
        "print('values:',mice['Genotype'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "values in Genotype column: 2\n",
            "values: ['Control' 'Ts65Dn']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_FrjVe7B95Gm"
      },
      "source": [
        "**MEMANTINE--MICE INJECTED WITH THE DRUG MEMANTINE**\n",
        "\n",
        "**SALINE--MICE INJECTED WITH SALINE WATER **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qmo9bYuo8fq1",
        "outputId": "55c3066d-480e-4a94-d504-249dd14b3ebd"
      },
      "source": [
        "print('values in Treatment column:',mice['Treatment'].nunique())\n",
        "print('values:',mice['Treatment'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "values in Treatment column: 2\n",
            "values: ['Memantine' 'Saline']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzwELayN-LWu"
      },
      "source": [
        "**C/S--SIMULATED TO CONTEXT SHOCK**\n",
        "\n",
        "**S/C--SIMULATED TO SHOCK CONTEXT **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mani6ii48iYm",
        "outputId": "63c49e7e-de9b-4966-c878-4f1c924c5043"
      },
      "source": [
        "print('values in Behavior column:',mice['Behavior'].nunique())\n",
        "print('values:',mice['Behavior'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "values in Behavior column: 2\n",
            "values: ['C/S' 'S/C']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHIbxLts-cJP"
      },
      "source": [
        "C-CS-M-->CONTROL-CONTEXTSHOCK-MEMANTINE\n",
        "\n",
        "C-SC-M-->CONTROL-SHOCKCONTEXT-MEMANTINE\n",
        "\n",
        "C-CS-S-->CONTROL-CONTEXTSHOCK-SALINE\n",
        "\n",
        "C-SC-S-->CONTROL-SHOCKCONTEXT-SALINE\n",
        "\n",
        "T-CS-M-->TRISOMIC-CONTROLSHOCK-MEMANTINE\n",
        "\n",
        "T-SC-M-->TRISOMIC-SHOCKCONTEXT-MEMANTINE\n",
        "\n",
        "T-CS-S-->TRISOMIC-CONTROLSHOCK-SALINE\n",
        "\n",
        "T-SC-S-->TRISOMIC-SHOCKCONTEXT-SALINE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CCfvqvbN8_xa",
        "outputId": "e4e65ffd-90fa-4a38-cdfd-13534f632aec"
      },
      "source": [
        "print('values in class column:',mice['class'].nunique())\n",
        "print('values:',mice['class'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "values in class column: 8\n",
            "values: ['c-CS-m' 'c-SC-m' 'c-CS-s' 'c-SC-s' 't-CS-m' 't-SC-m' 't-CS-s' 't-SC-s']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DPySnu5WurzV"
      },
      "source": [
        "**ENCODING CATEGORICAL ATTRIBUTES TO NUMERICAL**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "3QaGoGVz8S4Y",
        "outputId": "f36fc0a0-0c13-4a8c-fec8-587bb52eb5ce"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "val=preprocessing.LabelEncoder()\n",
        "nmice['Genotype']= val.fit_transform(nmice['Genotype'])\n",
        "nmice['Treatment']= val.fit_transform(nmice['Treatment'])\n",
        "nmice['Behavior']= val.fit_transform(nmice['Behavior'])\n",
        "nmice"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MouseID</th>\n",
              "      <th>DYRK1A_N</th>\n",
              "      <th>ITSN1_N</th>\n",
              "      <th>BDNF_N</th>\n",
              "      <th>NR1_N</th>\n",
              "      <th>NR2A_N</th>\n",
              "      <th>pAKT_N</th>\n",
              "      <th>pBRAF_N</th>\n",
              "      <th>pCAMKII_N</th>\n",
              "      <th>pCREB_N</th>\n",
              "      <th>pELK_N</th>\n",
              "      <th>pERK_N</th>\n",
              "      <th>pJNK_N</th>\n",
              "      <th>PKCA_N</th>\n",
              "      <th>pMEK_N</th>\n",
              "      <th>pNR1_N</th>\n",
              "      <th>pNR2A_N</th>\n",
              "      <th>pNR2B_N</th>\n",
              "      <th>pPKCAB_N</th>\n",
              "      <th>pRSK_N</th>\n",
              "      <th>AKT_N</th>\n",
              "      <th>BRAF_N</th>\n",
              "      <th>CAMKII_N</th>\n",
              "      <th>CREB_N</th>\n",
              "      <th>ELK_N</th>\n",
              "      <th>ERK_N</th>\n",
              "      <th>GSK3B_N</th>\n",
              "      <th>JNK_N</th>\n",
              "      <th>MEK_N</th>\n",
              "      <th>TRKA_N</th>\n",
              "      <th>RSK_N</th>\n",
              "      <th>APP_N</th>\n",
              "      <th>Bcatenin_N</th>\n",
              "      <th>SOD1_N</th>\n",
              "      <th>MTOR_N</th>\n",
              "      <th>P38_N</th>\n",
              "      <th>pMTOR_N</th>\n",
              "      <th>DSCR1_N</th>\n",
              "      <th>AMPKA_N</th>\n",
              "      <th>NR2B_N</th>\n",
              "      <th>...</th>\n",
              "      <th>TIAM1_N</th>\n",
              "      <th>pP70S6_N</th>\n",
              "      <th>NUMB_N</th>\n",
              "      <th>P70S6_N</th>\n",
              "      <th>pGSK3B_N</th>\n",
              "      <th>pPKCG_N</th>\n",
              "      <th>CDK5_N</th>\n",
              "      <th>S6_N</th>\n",
              "      <th>ADARB1_N</th>\n",
              "      <th>AcetylH3K9_N</th>\n",
              "      <th>RRP1_N</th>\n",
              "      <th>BAX_N</th>\n",
              "      <th>ARC_N</th>\n",
              "      <th>ERBB4_N</th>\n",
              "      <th>nNOS_N</th>\n",
              "      <th>Tau_N</th>\n",
              "      <th>GFAP_N</th>\n",
              "      <th>GluR3_N</th>\n",
              "      <th>GluR4_N</th>\n",
              "      <th>IL1B_N</th>\n",
              "      <th>P3525_N</th>\n",
              "      <th>pCASP9_N</th>\n",
              "      <th>PSD95_N</th>\n",
              "      <th>SNCA_N</th>\n",
              "      <th>Ubiquitin_N</th>\n",
              "      <th>pGSK3B_Tyr216_N</th>\n",
              "      <th>SHH_N</th>\n",
              "      <th>BAD_N</th>\n",
              "      <th>BCL2_N</th>\n",
              "      <th>pS6_N</th>\n",
              "      <th>pCFOS_N</th>\n",
              "      <th>SYP_N</th>\n",
              "      <th>H3AcK18_N</th>\n",
              "      <th>EGR1_N</th>\n",
              "      <th>H3MeK4_N</th>\n",
              "      <th>CaNA_N</th>\n",
              "      <th>Genotype</th>\n",
              "      <th>Treatment</th>\n",
              "      <th>Behavior</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>309_1</td>\n",
              "      <td>0.503644</td>\n",
              "      <td>0.747193</td>\n",
              "      <td>0.430175</td>\n",
              "      <td>2.816329</td>\n",
              "      <td>5.990152</td>\n",
              "      <td>0.218830</td>\n",
              "      <td>0.177565</td>\n",
              "      <td>2.373744</td>\n",
              "      <td>0.232224</td>\n",
              "      <td>1.750936</td>\n",
              "      <td>0.687906</td>\n",
              "      <td>0.306382</td>\n",
              "      <td>0.402698</td>\n",
              "      <td>0.296927</td>\n",
              "      <td>1.022060</td>\n",
              "      <td>0.605673</td>\n",
              "      <td>1.877684</td>\n",
              "      <td>2.308745</td>\n",
              "      <td>0.441599</td>\n",
              "      <td>0.859366</td>\n",
              "      <td>0.416289</td>\n",
              "      <td>0.369608</td>\n",
              "      <td>0.178944</td>\n",
              "      <td>1.866358</td>\n",
              "      <td>3.685247</td>\n",
              "      <td>1.537227</td>\n",
              "      <td>0.264526</td>\n",
              "      <td>0.319677</td>\n",
              "      <td>0.813866</td>\n",
              "      <td>0.165846</td>\n",
              "      <td>0.453910</td>\n",
              "      <td>3.037621</td>\n",
              "      <td>0.369510</td>\n",
              "      <td>0.458539</td>\n",
              "      <td>0.335336</td>\n",
              "      <td>0.825192</td>\n",
              "      <td>0.576916</td>\n",
              "      <td>0.448099</td>\n",
              "      <td>0.586271</td>\n",
              "      <td>...</td>\n",
              "      <td>0.482864</td>\n",
              "      <td>0.294170</td>\n",
              "      <td>0.182150</td>\n",
              "      <td>0.842725</td>\n",
              "      <td>0.192608</td>\n",
              "      <td>1.443091</td>\n",
              "      <td>0.294700</td>\n",
              "      <td>0.354605</td>\n",
              "      <td>1.339070</td>\n",
              "      <td>0.170119</td>\n",
              "      <td>0.159102</td>\n",
              "      <td>0.188852</td>\n",
              "      <td>0.106305</td>\n",
              "      <td>0.144989</td>\n",
              "      <td>0.176668</td>\n",
              "      <td>0.125190</td>\n",
              "      <td>0.115291</td>\n",
              "      <td>0.228043</td>\n",
              "      <td>0.142756</td>\n",
              "      <td>0.430957</td>\n",
              "      <td>0.247538</td>\n",
              "      <td>1.603310</td>\n",
              "      <td>2.014875</td>\n",
              "      <td>0.108234</td>\n",
              "      <td>1.044979</td>\n",
              "      <td>0.831557</td>\n",
              "      <td>0.188852</td>\n",
              "      <td>0.122652</td>\n",
              "      <td>0.134762</td>\n",
              "      <td>0.106305</td>\n",
              "      <td>0.108336</td>\n",
              "      <td>0.427099</td>\n",
              "      <td>0.114783</td>\n",
              "      <td>0.131790</td>\n",
              "      <td>0.128186</td>\n",
              "      <td>1.675652</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>309_2</td>\n",
              "      <td>0.514617</td>\n",
              "      <td>0.689064</td>\n",
              "      <td>0.411770</td>\n",
              "      <td>2.789514</td>\n",
              "      <td>5.685038</td>\n",
              "      <td>0.211636</td>\n",
              "      <td>0.172817</td>\n",
              "      <td>2.292150</td>\n",
              "      <td>0.226972</td>\n",
              "      <td>1.596377</td>\n",
              "      <td>0.695006</td>\n",
              "      <td>0.299051</td>\n",
              "      <td>0.385987</td>\n",
              "      <td>0.281319</td>\n",
              "      <td>0.956676</td>\n",
              "      <td>0.587559</td>\n",
              "      <td>1.725774</td>\n",
              "      <td>2.043037</td>\n",
              "      <td>0.445222</td>\n",
              "      <td>0.834659</td>\n",
              "      <td>0.400364</td>\n",
              "      <td>0.356178</td>\n",
              "      <td>0.173680</td>\n",
              "      <td>1.761047</td>\n",
              "      <td>3.485287</td>\n",
              "      <td>1.509249</td>\n",
              "      <td>0.255727</td>\n",
              "      <td>0.304419</td>\n",
              "      <td>0.780504</td>\n",
              "      <td>0.157194</td>\n",
              "      <td>0.430940</td>\n",
              "      <td>2.921882</td>\n",
              "      <td>0.342279</td>\n",
              "      <td>0.423560</td>\n",
              "      <td>0.324835</td>\n",
              "      <td>0.761718</td>\n",
              "      <td>0.545097</td>\n",
              "      <td>0.420876</td>\n",
              "      <td>0.545097</td>\n",
              "      <td>...</td>\n",
              "      <td>0.454519</td>\n",
              "      <td>0.276431</td>\n",
              "      <td>0.182086</td>\n",
              "      <td>0.847615</td>\n",
              "      <td>0.194815</td>\n",
              "      <td>1.439460</td>\n",
              "      <td>0.294060</td>\n",
              "      <td>0.354548</td>\n",
              "      <td>1.306323</td>\n",
              "      <td>0.171427</td>\n",
              "      <td>0.158129</td>\n",
              "      <td>0.184570</td>\n",
              "      <td>0.106592</td>\n",
              "      <td>0.150471</td>\n",
              "      <td>0.178309</td>\n",
              "      <td>0.134275</td>\n",
              "      <td>0.118235</td>\n",
              "      <td>0.238073</td>\n",
              "      <td>0.142037</td>\n",
              "      <td>0.457156</td>\n",
              "      <td>0.257632</td>\n",
              "      <td>1.671738</td>\n",
              "      <td>2.004605</td>\n",
              "      <td>0.109749</td>\n",
              "      <td>1.009883</td>\n",
              "      <td>0.849270</td>\n",
              "      <td>0.200404</td>\n",
              "      <td>0.116682</td>\n",
              "      <td>0.134762</td>\n",
              "      <td>0.106592</td>\n",
              "      <td>0.104315</td>\n",
              "      <td>0.441581</td>\n",
              "      <td>0.111974</td>\n",
              "      <td>0.135103</td>\n",
              "      <td>0.131119</td>\n",
              "      <td>1.743610</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>309_3</td>\n",
              "      <td>0.509183</td>\n",
              "      <td>0.730247</td>\n",
              "      <td>0.418309</td>\n",
              "      <td>2.687201</td>\n",
              "      <td>5.622059</td>\n",
              "      <td>0.209011</td>\n",
              "      <td>0.175722</td>\n",
              "      <td>2.283337</td>\n",
              "      <td>0.230247</td>\n",
              "      <td>1.561316</td>\n",
              "      <td>0.677348</td>\n",
              "      <td>0.291276</td>\n",
              "      <td>0.381002</td>\n",
              "      <td>0.281710</td>\n",
              "      <td>1.003635</td>\n",
              "      <td>0.602449</td>\n",
              "      <td>1.731873</td>\n",
              "      <td>2.017984</td>\n",
              "      <td>0.467668</td>\n",
              "      <td>0.814329</td>\n",
              "      <td>0.399847</td>\n",
              "      <td>0.368089</td>\n",
              "      <td>0.173905</td>\n",
              "      <td>1.765544</td>\n",
              "      <td>3.571456</td>\n",
              "      <td>1.501244</td>\n",
              "      <td>0.259614</td>\n",
              "      <td>0.311747</td>\n",
              "      <td>0.785154</td>\n",
              "      <td>0.160895</td>\n",
              "      <td>0.423187</td>\n",
              "      <td>2.944136</td>\n",
              "      <td>0.343696</td>\n",
              "      <td>0.425005</td>\n",
              "      <td>0.324852</td>\n",
              "      <td>0.757031</td>\n",
              "      <td>0.543620</td>\n",
              "      <td>0.404630</td>\n",
              "      <td>0.552994</td>\n",
              "      <td>...</td>\n",
              "      <td>0.447197</td>\n",
              "      <td>0.256648</td>\n",
              "      <td>0.184388</td>\n",
              "      <td>0.856166</td>\n",
              "      <td>0.200737</td>\n",
              "      <td>1.524364</td>\n",
              "      <td>0.301881</td>\n",
              "      <td>0.386087</td>\n",
              "      <td>1.279600</td>\n",
              "      <td>0.185456</td>\n",
              "      <td>0.148696</td>\n",
              "      <td>0.190532</td>\n",
              "      <td>0.108303</td>\n",
              "      <td>0.145330</td>\n",
              "      <td>0.176213</td>\n",
              "      <td>0.132560</td>\n",
              "      <td>0.117760</td>\n",
              "      <td>0.244817</td>\n",
              "      <td>0.142445</td>\n",
              "      <td>0.510472</td>\n",
              "      <td>0.255343</td>\n",
              "      <td>1.663550</td>\n",
              "      <td>2.016831</td>\n",
              "      <td>0.108196</td>\n",
              "      <td>0.996848</td>\n",
              "      <td>0.846709</td>\n",
              "      <td>0.193685</td>\n",
              "      <td>0.118508</td>\n",
              "      <td>0.134762</td>\n",
              "      <td>0.108303</td>\n",
              "      <td>0.106219</td>\n",
              "      <td>0.435777</td>\n",
              "      <td>0.111883</td>\n",
              "      <td>0.133362</td>\n",
              "      <td>0.127431</td>\n",
              "      <td>1.926427</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>309_4</td>\n",
              "      <td>0.442107</td>\n",
              "      <td>0.617076</td>\n",
              "      <td>0.358626</td>\n",
              "      <td>2.466947</td>\n",
              "      <td>4.979503</td>\n",
              "      <td>0.222886</td>\n",
              "      <td>0.176463</td>\n",
              "      <td>2.152301</td>\n",
              "      <td>0.207004</td>\n",
              "      <td>1.595086</td>\n",
              "      <td>0.583277</td>\n",
              "      <td>0.296729</td>\n",
              "      <td>0.377087</td>\n",
              "      <td>0.313832</td>\n",
              "      <td>0.875390</td>\n",
              "      <td>0.520293</td>\n",
              "      <td>1.566852</td>\n",
              "      <td>2.132754</td>\n",
              "      <td>0.477671</td>\n",
              "      <td>0.727705</td>\n",
              "      <td>0.385639</td>\n",
              "      <td>0.362970</td>\n",
              "      <td>0.179449</td>\n",
              "      <td>1.286277</td>\n",
              "      <td>2.970137</td>\n",
              "      <td>1.419710</td>\n",
              "      <td>0.259536</td>\n",
              "      <td>0.279218</td>\n",
              "      <td>0.734492</td>\n",
              "      <td>0.162210</td>\n",
              "      <td>0.410615</td>\n",
              "      <td>2.500204</td>\n",
              "      <td>0.344509</td>\n",
              "      <td>0.429211</td>\n",
              "      <td>0.330121</td>\n",
              "      <td>0.746980</td>\n",
              "      <td>0.546763</td>\n",
              "      <td>0.386860</td>\n",
              "      <td>0.547849</td>\n",
              "      <td>...</td>\n",
              "      <td>0.442650</td>\n",
              "      <td>0.398534</td>\n",
              "      <td>0.161768</td>\n",
              "      <td>0.760234</td>\n",
              "      <td>0.184169</td>\n",
              "      <td>1.612382</td>\n",
              "      <td>0.296382</td>\n",
              "      <td>0.290680</td>\n",
              "      <td>1.198765</td>\n",
              "      <td>0.159799</td>\n",
              "      <td>0.166112</td>\n",
              "      <td>0.185323</td>\n",
              "      <td>0.103184</td>\n",
              "      <td>0.140656</td>\n",
              "      <td>0.163804</td>\n",
              "      <td>0.123210</td>\n",
              "      <td>0.117439</td>\n",
              "      <td>0.234947</td>\n",
              "      <td>0.145068</td>\n",
              "      <td>0.430996</td>\n",
              "      <td>0.251103</td>\n",
              "      <td>1.484624</td>\n",
              "      <td>1.957233</td>\n",
              "      <td>0.119883</td>\n",
              "      <td>0.990225</td>\n",
              "      <td>0.833277</td>\n",
              "      <td>0.192112</td>\n",
              "      <td>0.132781</td>\n",
              "      <td>0.134762</td>\n",
              "      <td>0.103184</td>\n",
              "      <td>0.111262</td>\n",
              "      <td>0.391691</td>\n",
              "      <td>0.130405</td>\n",
              "      <td>0.147444</td>\n",
              "      <td>0.146901</td>\n",
              "      <td>1.700563</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>309_5</td>\n",
              "      <td>0.434940</td>\n",
              "      <td>0.617430</td>\n",
              "      <td>0.358802</td>\n",
              "      <td>2.365785</td>\n",
              "      <td>4.718679</td>\n",
              "      <td>0.213106</td>\n",
              "      <td>0.173627</td>\n",
              "      <td>2.134014</td>\n",
              "      <td>0.192158</td>\n",
              "      <td>1.504230</td>\n",
              "      <td>0.550960</td>\n",
              "      <td>0.286961</td>\n",
              "      <td>0.363502</td>\n",
              "      <td>0.277964</td>\n",
              "      <td>0.864912</td>\n",
              "      <td>0.507990</td>\n",
              "      <td>1.480059</td>\n",
              "      <td>2.013697</td>\n",
              "      <td>0.483416</td>\n",
              "      <td>0.687794</td>\n",
              "      <td>0.367531</td>\n",
              "      <td>0.355311</td>\n",
              "      <td>0.174836</td>\n",
              "      <td>1.324695</td>\n",
              "      <td>2.896334</td>\n",
              "      <td>1.359876</td>\n",
              "      <td>0.250705</td>\n",
              "      <td>0.273667</td>\n",
              "      <td>0.702699</td>\n",
              "      <td>0.154827</td>\n",
              "      <td>0.398550</td>\n",
              "      <td>2.456560</td>\n",
              "      <td>0.329126</td>\n",
              "      <td>0.408755</td>\n",
              "      <td>0.313415</td>\n",
              "      <td>0.691956</td>\n",
              "      <td>0.536860</td>\n",
              "      <td>0.360816</td>\n",
              "      <td>0.512824</td>\n",
              "      <td>...</td>\n",
              "      <td>0.419095</td>\n",
              "      <td>0.393447</td>\n",
              "      <td>0.160200</td>\n",
              "      <td>0.768113</td>\n",
              "      <td>0.185718</td>\n",
              "      <td>1.645807</td>\n",
              "      <td>0.296829</td>\n",
              "      <td>0.309345</td>\n",
              "      <td>1.206995</td>\n",
              "      <td>0.164650</td>\n",
              "      <td>0.160687</td>\n",
              "      <td>0.188221</td>\n",
              "      <td>0.104784</td>\n",
              "      <td>0.141983</td>\n",
              "      <td>0.167710</td>\n",
              "      <td>0.136838</td>\n",
              "      <td>0.116048</td>\n",
              "      <td>0.255528</td>\n",
              "      <td>0.140871</td>\n",
              "      <td>0.481227</td>\n",
              "      <td>0.251773</td>\n",
              "      <td>1.534835</td>\n",
              "      <td>2.009109</td>\n",
              "      <td>0.119524</td>\n",
              "      <td>0.997775</td>\n",
              "      <td>0.878668</td>\n",
              "      <td>0.205604</td>\n",
              "      <td>0.129954</td>\n",
              "      <td>0.134762</td>\n",
              "      <td>0.104784</td>\n",
              "      <td>0.110694</td>\n",
              "      <td>0.434154</td>\n",
              "      <td>0.118481</td>\n",
              "      <td>0.140314</td>\n",
              "      <td>0.148380</td>\n",
              "      <td>1.839730</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>c-CS-m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1075</th>\n",
              "      <td>J3295_11</td>\n",
              "      <td>0.254860</td>\n",
              "      <td>0.463591</td>\n",
              "      <td>0.254860</td>\n",
              "      <td>2.092082</td>\n",
              "      <td>2.600035</td>\n",
              "      <td>0.211736</td>\n",
              "      <td>0.171262</td>\n",
              "      <td>2.483740</td>\n",
              "      <td>0.207317</td>\n",
              "      <td>1.057971</td>\n",
              "      <td>0.265642</td>\n",
              "      <td>0.294097</td>\n",
              "      <td>0.249912</td>\n",
              "      <td>0.261223</td>\n",
              "      <td>0.746200</td>\n",
              "      <td>0.510604</td>\n",
              "      <td>1.220926</td>\n",
              "      <td>1.241958</td>\n",
              "      <td>0.422764</td>\n",
              "      <td>0.638211</td>\n",
              "      <td>0.255744</td>\n",
              "      <td>0.330859</td>\n",
              "      <td>0.190173</td>\n",
              "      <td>0.896430</td>\n",
              "      <td>1.822906</td>\n",
              "      <td>0.993107</td>\n",
              "      <td>0.236303</td>\n",
              "      <td>0.249205</td>\n",
              "      <td>0.641746</td>\n",
              "      <td>0.165606</td>\n",
              "      <td>0.372216</td>\n",
              "      <td>1.828208</td>\n",
              "      <td>0.820078</td>\n",
              "      <td>0.380170</td>\n",
              "      <td>0.320431</td>\n",
              "      <td>0.599328</td>\n",
              "      <td>0.508130</td>\n",
              "      <td>0.270060</td>\n",
              "      <td>0.437964</td>\n",
              "      <td>...</td>\n",
              "      <td>0.340756</td>\n",
              "      <td>0.527041</td>\n",
              "      <td>0.209433</td>\n",
              "      <td>0.787900</td>\n",
              "      <td>0.178130</td>\n",
              "      <td>2.630825</td>\n",
              "      <td>0.319062</td>\n",
              "      <td>0.654548</td>\n",
              "      <td>0.737226</td>\n",
              "      <td>0.532987</td>\n",
              "      <td>0.196659</td>\n",
              "      <td>0.182762</td>\n",
              "      <td>0.115806</td>\n",
              "      <td>0.160303</td>\n",
              "      <td>0.189360</td>\n",
              "      <td>0.411286</td>\n",
              "      <td>0.134896</td>\n",
              "      <td>0.207748</td>\n",
              "      <td>0.134475</td>\n",
              "      <td>0.503650</td>\n",
              "      <td>0.326362</td>\n",
              "      <td>1.323554</td>\n",
              "      <td>2.578046</td>\n",
              "      <td>0.167181</td>\n",
              "      <td>1.261651</td>\n",
              "      <td>0.962942</td>\n",
              "      <td>0.275547</td>\n",
              "      <td>0.190483</td>\n",
              "      <td>0.134762</td>\n",
              "      <td>0.115806</td>\n",
              "      <td>0.183324</td>\n",
              "      <td>0.374088</td>\n",
              "      <td>0.318782</td>\n",
              "      <td>0.204660</td>\n",
              "      <td>0.328327</td>\n",
              "      <td>1.364823</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>t-SC-s</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1076</th>\n",
              "      <td>J3295_12</td>\n",
              "      <td>0.272198</td>\n",
              "      <td>0.474163</td>\n",
              "      <td>0.251638</td>\n",
              "      <td>2.161390</td>\n",
              "      <td>2.801492</td>\n",
              "      <td>0.251274</td>\n",
              "      <td>0.182496</td>\n",
              "      <td>2.512737</td>\n",
              "      <td>0.216339</td>\n",
              "      <td>1.081150</td>\n",
              "      <td>0.270378</td>\n",
              "      <td>0.285116</td>\n",
              "      <td>0.249818</td>\n",
              "      <td>0.252547</td>\n",
              "      <td>0.749818</td>\n",
              "      <td>0.524381</td>\n",
              "      <td>1.218705</td>\n",
              "      <td>1.361354</td>\n",
              "      <td>0.415211</td>\n",
              "      <td>0.645197</td>\n",
              "      <td>0.252001</td>\n",
              "      <td>0.338610</td>\n",
              "      <td>0.181223</td>\n",
              "      <td>0.958879</td>\n",
              "      <td>1.879913</td>\n",
              "      <td>0.974891</td>\n",
              "      <td>0.245451</td>\n",
              "      <td>0.262191</td>\n",
              "      <td>0.693595</td>\n",
              "      <td>0.191594</td>\n",
              "      <td>0.360990</td>\n",
              "      <td>1.883370</td>\n",
              "      <td>0.854258</td>\n",
              "      <td>0.380277</td>\n",
              "      <td>0.338246</td>\n",
              "      <td>0.614629</td>\n",
              "      <td>0.519105</td>\n",
              "      <td>0.273472</td>\n",
              "      <td>0.580058</td>\n",
              "      <td>...</td>\n",
              "      <td>0.346252</td>\n",
              "      <td>0.518377</td>\n",
              "      <td>0.194333</td>\n",
              "      <td>0.763096</td>\n",
              "      <td>0.170422</td>\n",
              "      <td>2.593227</td>\n",
              "      <td>0.318867</td>\n",
              "      <td>0.632066</td>\n",
              "      <td>0.756047</td>\n",
              "      <td>0.546648</td>\n",
              "      <td>0.188390</td>\n",
              "      <td>0.166966</td>\n",
              "      <td>0.113614</td>\n",
              "      <td>0.161576</td>\n",
              "      <td>0.187146</td>\n",
              "      <td>0.402073</td>\n",
              "      <td>0.130615</td>\n",
              "      <td>0.205114</td>\n",
              "      <td>0.122184</td>\n",
              "      <td>0.512647</td>\n",
              "      <td>0.344160</td>\n",
              "      <td>1.275605</td>\n",
              "      <td>2.534347</td>\n",
              "      <td>0.169592</td>\n",
              "      <td>1.254872</td>\n",
              "      <td>0.983690</td>\n",
              "      <td>0.283207</td>\n",
              "      <td>0.190463</td>\n",
              "      <td>0.134762</td>\n",
              "      <td>0.113614</td>\n",
              "      <td>0.175674</td>\n",
              "      <td>0.375259</td>\n",
              "      <td>0.325639</td>\n",
              "      <td>0.200415</td>\n",
              "      <td>0.293435</td>\n",
              "      <td>1.364478</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>t-SC-s</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1077</th>\n",
              "      <td>J3295_13</td>\n",
              "      <td>0.228700</td>\n",
              "      <td>0.395179</td>\n",
              "      <td>0.234118</td>\n",
              "      <td>1.733184</td>\n",
              "      <td>2.220852</td>\n",
              "      <td>0.220665</td>\n",
              "      <td>0.161435</td>\n",
              "      <td>1.989723</td>\n",
              "      <td>0.185164</td>\n",
              "      <td>0.884342</td>\n",
              "      <td>0.255045</td>\n",
              "      <td>0.245703</td>\n",
              "      <td>0.221413</td>\n",
              "      <td>0.255792</td>\n",
              "      <td>0.636024</td>\n",
              "      <td>0.442638</td>\n",
              "      <td>1.015695</td>\n",
              "      <td>1.065022</td>\n",
              "      <td>0.408259</td>\n",
              "      <td>0.540172</td>\n",
              "      <td>0.238042</td>\n",
              "      <td>0.326981</td>\n",
              "      <td>0.212631</td>\n",
              "      <td>0.762892</td>\n",
              "      <td>1.425262</td>\n",
              "      <td>0.818199</td>\n",
              "      <td>0.216741</td>\n",
              "      <td>0.235426</td>\n",
              "      <td>0.559043</td>\n",
              "      <td>0.168161</td>\n",
              "      <td>0.309978</td>\n",
              "      <td>1.494208</td>\n",
              "      <td>0.661809</td>\n",
              "      <td>0.337444</td>\n",
              "      <td>0.309978</td>\n",
              "      <td>0.510650</td>\n",
              "      <td>0.463378</td>\n",
              "      <td>0.234679</td>\n",
              "      <td>0.400037</td>\n",
              "      <td>...</td>\n",
              "      <td>0.292788</td>\n",
              "      <td>0.460202</td>\n",
              "      <td>0.196736</td>\n",
              "      <td>0.804896</td>\n",
              "      <td>0.170807</td>\n",
              "      <td>2.628286</td>\n",
              "      <td>0.313327</td>\n",
              "      <td>0.669810</td>\n",
              "      <td>0.764098</td>\n",
              "      <td>0.536899</td>\n",
              "      <td>0.201269</td>\n",
              "      <td>0.169175</td>\n",
              "      <td>0.118948</td>\n",
              "      <td>0.174252</td>\n",
              "      <td>0.185131</td>\n",
              "      <td>0.395648</td>\n",
              "      <td>0.137081</td>\n",
              "      <td>0.201088</td>\n",
              "      <td>0.126927</td>\n",
              "      <td>0.631188</td>\n",
              "      <td>0.358114</td>\n",
              "      <td>1.437534</td>\n",
              "      <td>2.544515</td>\n",
              "      <td>0.179692</td>\n",
              "      <td>1.242248</td>\n",
              "      <td>0.976609</td>\n",
              "      <td>0.290843</td>\n",
              "      <td>0.216682</td>\n",
              "      <td>0.134762</td>\n",
              "      <td>0.118948</td>\n",
              "      <td>0.158296</td>\n",
              "      <td>0.422121</td>\n",
              "      <td>0.321306</td>\n",
              "      <td>0.229193</td>\n",
              "      <td>0.355213</td>\n",
              "      <td>1.430825</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>t-SC-s</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1078</th>\n",
              "      <td>J3295_14</td>\n",
              "      <td>0.221242</td>\n",
              "      <td>0.412894</td>\n",
              "      <td>0.243974</td>\n",
              "      <td>1.876347</td>\n",
              "      <td>2.384088</td>\n",
              "      <td>0.208897</td>\n",
              "      <td>0.173623</td>\n",
              "      <td>2.086028</td>\n",
              "      <td>0.192044</td>\n",
              "      <td>0.922595</td>\n",
              "      <td>0.230649</td>\n",
              "      <td>0.263179</td>\n",
              "      <td>0.224378</td>\n",
              "      <td>0.277484</td>\n",
              "      <td>0.665099</td>\n",
              "      <td>0.479522</td>\n",
              "      <td>1.077014</td>\n",
              "      <td>1.115030</td>\n",
              "      <td>0.434646</td>\n",
              "      <td>0.564766</td>\n",
              "      <td>0.256712</td>\n",
              "      <td>0.313149</td>\n",
              "      <td>0.172252</td>\n",
              "      <td>0.781893</td>\n",
              "      <td>1.558887</td>\n",
              "      <td>0.872820</td>\n",
              "      <td>0.238095</td>\n",
              "      <td>0.270429</td>\n",
              "      <td>0.585146</td>\n",
              "      <td>0.163433</td>\n",
              "      <td>0.341172</td>\n",
              "      <td>1.571820</td>\n",
              "      <td>0.698413</td>\n",
              "      <td>0.356457</td>\n",
              "      <td>0.308446</td>\n",
              "      <td>0.545757</td>\n",
              "      <td>0.480502</td>\n",
              "      <td>0.258083</td>\n",
              "      <td>0.426612</td>\n",
              "      <td>...</td>\n",
              "      <td>0.301587</td>\n",
              "      <td>0.490496</td>\n",
              "      <td>0.197567</td>\n",
              "      <td>0.784819</td>\n",
              "      <td>0.175413</td>\n",
              "      <td>2.659706</td>\n",
              "      <td>0.341021</td>\n",
              "      <td>0.642637</td>\n",
              "      <td>0.783185</td>\n",
              "      <td>0.538224</td>\n",
              "      <td>0.212094</td>\n",
              "      <td>0.161431</td>\n",
              "      <td>0.125295</td>\n",
              "      <td>0.172508</td>\n",
              "      <td>0.193753</td>\n",
              "      <td>0.414200</td>\n",
              "      <td>0.149265</td>\n",
              "      <td>0.204467</td>\n",
              "      <td>0.124569</td>\n",
              "      <td>0.621754</td>\n",
              "      <td>0.352279</td>\n",
              "      <td>1.498820</td>\n",
              "      <td>2.609769</td>\n",
              "      <td>0.185037</td>\n",
              "      <td>1.301071</td>\n",
              "      <td>0.989286</td>\n",
              "      <td>0.306701</td>\n",
              "      <td>0.222263</td>\n",
              "      <td>0.134762</td>\n",
              "      <td>0.125295</td>\n",
              "      <td>0.196296</td>\n",
              "      <td>0.397676</td>\n",
              "      <td>0.335936</td>\n",
              "      <td>0.251317</td>\n",
              "      <td>0.365353</td>\n",
              "      <td>1.404031</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>t-SC-s</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1079</th>\n",
              "      <td>J3295_15</td>\n",
              "      <td>0.302626</td>\n",
              "      <td>0.461059</td>\n",
              "      <td>0.256564</td>\n",
              "      <td>2.092790</td>\n",
              "      <td>2.594348</td>\n",
              "      <td>0.251001</td>\n",
              "      <td>0.191811</td>\n",
              "      <td>2.361816</td>\n",
              "      <td>0.223632</td>\n",
              "      <td>1.064085</td>\n",
              "      <td>0.276146</td>\n",
              "      <td>0.293725</td>\n",
              "      <td>0.275478</td>\n",
              "      <td>0.315754</td>\n",
              "      <td>0.713173</td>\n",
              "      <td>0.502225</td>\n",
              "      <td>1.231865</td>\n",
              "      <td>1.282377</td>\n",
              "      <td>0.429239</td>\n",
              "      <td>0.629506</td>\n",
              "      <td>0.311972</td>\n",
              "      <td>0.349800</td>\n",
              "      <td>0.187583</td>\n",
              "      <td>0.884735</td>\n",
              "      <td>1.785937</td>\n",
              "      <td>0.970182</td>\n",
              "      <td>0.287049</td>\n",
              "      <td>0.281709</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.190031</td>\n",
              "      <td>0.402982</td>\n",
              "      <td>1.742768</td>\n",
              "      <td>0.786827</td>\n",
              "      <td>0.399866</td>\n",
              "      <td>0.339119</td>\n",
              "      <td>0.602804</td>\n",
              "      <td>0.533823</td>\n",
              "      <td>0.287717</td>\n",
              "      <td>0.637962</td>\n",
              "      <td>...</td>\n",
              "      <td>0.368269</td>\n",
              "      <td>0.546729</td>\n",
              "      <td>0.188807</td>\n",
              "      <td>0.772752</td>\n",
              "      <td>0.172716</td>\n",
              "      <td>2.654926</td>\n",
              "      <td>0.317003</td>\n",
              "      <td>0.631682</td>\n",
              "      <td>0.736635</td>\n",
              "      <td>0.535312</td>\n",
              "      <td>0.193992</td>\n",
              "      <td>0.172180</td>\n",
              "      <td>0.118899</td>\n",
              "      <td>0.172001</td>\n",
              "      <td>0.191311</td>\n",
              "      <td>0.393170</td>\n",
              "      <td>0.140533</td>\n",
              "      <td>0.204363</td>\n",
              "      <td>0.121402</td>\n",
              "      <td>0.630252</td>\n",
              "      <td>0.356517</td>\n",
              "      <td>1.490077</td>\n",
              "      <td>2.526372</td>\n",
              "      <td>0.184516</td>\n",
              "      <td>1.267120</td>\n",
              "      <td>1.020383</td>\n",
              "      <td>0.292330</td>\n",
              "      <td>0.227606</td>\n",
              "      <td>0.134762</td>\n",
              "      <td>0.118899</td>\n",
              "      <td>0.187556</td>\n",
              "      <td>0.420347</td>\n",
              "      <td>0.335062</td>\n",
              "      <td>0.252995</td>\n",
              "      <td>0.365278</td>\n",
              "      <td>1.370999</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>t-SC-s</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1080 rows × 82 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       MouseID  DYRK1A_N   ITSN1_N  ...  Treatment  Behavior   class\n",
              "0        309_1  0.503644  0.747193  ...          0         0  c-CS-m\n",
              "1        309_2  0.514617  0.689064  ...          0         0  c-CS-m\n",
              "2        309_3  0.509183  0.730247  ...          0         0  c-CS-m\n",
              "3        309_4  0.442107  0.617076  ...          0         0  c-CS-m\n",
              "4        309_5  0.434940  0.617430  ...          0         0  c-CS-m\n",
              "...        ...       ...       ...  ...        ...       ...     ...\n",
              "1075  J3295_11  0.254860  0.463591  ...          1         1  t-SC-s\n",
              "1076  J3295_12  0.272198  0.474163  ...          1         1  t-SC-s\n",
              "1077  J3295_13  0.228700  0.395179  ...          1         1  t-SC-s\n",
              "1078  J3295_14  0.221242  0.412894  ...          1         1  t-SC-s\n",
              "1079  J3295_15  0.302626  0.461059  ...          1         1  t-SC-s\n",
              "\n",
              "[1080 rows x 82 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XlU-fqeT_-n9"
      },
      "source": [
        "**IDENTIFICATION OF NUMBER OF RECORDS IN EACH CLASS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "rLJJpJyy__V4",
        "outputId": "26082bc9-ef59-4b0b-b85f-65e8b73a1534"
      },
      "source": [
        "sns.countplot(x='class',data=mice)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe246c98950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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McnWSF7fzTk1ye5Jbk9y8bnxMOd+cZFWb5bYkS3rm/U2S77QZb0hy/Lhy9mRyu278secnedsG5h/fZlqV5KYkf9OOH5rkujbXnUlOH2SuTcmZ5MD2Z7suy/KeeYckWdFepuamJJ9M8tRhZt1Y3na+23VTVNWc+qJ5l/TfAdu10/sBrwBeAnwL2KkdXwDsNaaMC4EfAE9rp3cG9mtvv5XmRPyu7fSuwFK365a/XYFFwG0zzHs58O112wbYCXhLe/u7wAva2/OAg4a8nTaU86vAkp7p57Xf9wDuAV7SM+8YYI8R/FzdroPMOewf2Ki/gOOBW4FbgE+tN29/4IfAvGnudzTwpT7WfzhwDXA5cBfwYeANwPXAKmD/AeQ8GLh5hpz3An84qpwj3K57Aiva530b8B+G8PMfxHaddU6a4nysve9H1pu3Ajhihvs9Ajyjj/XfTVPMNwMr2+f7VZoifOuAct4KvGia+5wBnNHn+geSc4Tb9cPAHe1zP2s2v5cj2q6DyTnbO26JX8BzgO8BC9rp3dabfxTwxRnuu3P7w/oe8AngZTMsdzjwaPufw040b67723beO4F/GEDOee0/knuBC4FXteO7Ao/0uS02O+eIt+u7gVN7nv8uQ/j5D2K7zjonG/5L8WHaPZhp5n2w/Q/si8BfAE+ZYbm7gf/U3j67/c9hF2AC+PGAcp4A/BT4CvDXwPx2/Av0/KW7kfUPJOcotiuwO80exbpXas7f1Hyj2K6DzLmtnVM4AvhcVf0EoKoe7veOVfUL4EU011WaAv45yZtmWPyGqrq/qh6n+evma+34Kpof/GblrKongMU0u4nfA86e5fHOzc3ZV94N2YTtegNwQvs8n1dVP9+EfH3lHNB2HUTOTVJVZwCTND+/Pwf+dQOLr3uz5yrguqr6eVVNAY8nmT+ALBcCzwY+R/OHx7VJdprFqoaasx99btefAr8Czk9yNPDLIWXZ3O06sJzbWin8jiTz2hM3Nyc5A7gdeEF7ie7fU1VPVNXVVXUacDLwmiQv7lnHUe2ij/fc7bc9079lFm8InCYn1bi+qv6O5p3dr6mqnwG/SPKH06xj6Dk3kHezt2tVrQBeSrNHc9EgTvIOY7sOKmeSM9ettx26naY8p1VVP6iq84Ajabb17km+2q7jkz2L9v6M1//5z+Z3c/2cVNX/q6oLqmoJsBZ47obyjyLnBvJu1natqrU0V2++DHglGy7kzcm5Wdt1oDlnu4uxJX7x5OGD3dvp3aZZ5lLgQzy5m7WI5oTogcABPct9CPj4NPc/HPiXnumrgcnp5s02J7AXcHDP9Enr1gu8jWYXc90J0Z2B44eRc8Tb9Vm0x/ppiqPvw1sj3q6zzkmzi3/PDPP+DLgReGY7vSNwUnv7FT3b9dk0V8yc7rzI3Tx56OxNvdu5d95m5lwM7NDefiZwf/t93QnRF/csezTTnBAdVM5RbNf29+AZ7e2nAQ9t6u/lKLbrIHNuUZe52FxVdXuSM4FrkjwB3ETzS9frJOCjwOokj9H8IpxCs1HPbXdd1wKrefIS3aPOuQNwVpK9aHYJp2heHQNwXpv1hiS/AX7TPp+hGdF2PRw4pX1Ov6A5YTzonIPYrrPOWVUPJflmktuAr1TVKT3zvpxkD+B/JwlQwAXt7ONoDnX9kmYbvqGaQ2FDsaGcwJ8A5yT5VTt9SlU9AJDkWJrt+wyav/hXMKC/rGebdwDbdRfg8iRPAQK8axg52fztOrCcXuZCktTZps8pSJI2jaUgSepYCpKkjqUgSepYCpKkjqUgbYYkp6e96qa0LbAUJEkdS0HaBGmuzX9rkluSfGq9eW9J8zkMtyT5fNpr3id5bZrr+d+SZEU79pwk17eXKbg1yQHjeD7S+nzzmtSnJM+huaLmH1fVT5LsBvwl8IuqOivJ7lX1ULvsh2iu+HluklXA4qq6L8n8qno0ybnAtVX16SQ70lxe4bFxPTdpHfcUpP5t7Gqxz03yf9oSeAPNtZgAvklz8by30FxuG5oPHvpAkvcCz7IQtKWwFKTBuQg4uaqeB/wt8BSAqnor8J+BfYAb2z2K/0nzORSPAV9OcsR4Iku/y1KQ+vd14LVJdgdoDx/12gW4P8kONHsKtMvtX1XXVdUHaS7Ct097me67qupjNJ+O9/yRPANpI7apq6RKwzTDVVjv7lnkvwDX0fzHfx1NSQB8pD2RHOAqmo8KfS9wXHu11QeA/zqSJyFthCeaJUkdDx9JkjqWgiSpYylIkjqWgiSpYylIkjqWgiSpYylIkjr/H1SMbQfxI/ULAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tcvSsa7NAT4f",
        "outputId": "12aa29fe-b38b-4fe7-9fa1-13f94cd35769"
      },
      "source": [
        "mice['class'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "c-CS-m    150\n",
              "c-SC-m    150\n",
              "c-SC-s    135\n",
              "t-SC-m    135\n",
              "c-CS-s    135\n",
              "t-SC-s    135\n",
              "t-CS-m    135\n",
              "t-CS-s    105\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u43txVuOoiAT"
      },
      "source": [
        "Control    570\n",
        "Ts65Dn     510 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "wX4jtPGIn6pF",
        "outputId": "4788575e-e83b-4afd-fec4-d8648876ca16"
      },
      "source": [
        "sns.countplot(x=\"Genotype\",data=mice)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe246c12610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hnIyNzyto1dH"
      },
      "source": [
        "S/C    555\n",
        "C/S    525"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "QbyuAI5-kYKh",
        "outputId": "037fd59c-4969-4ba8-b6f2-f2a3293eb9fa"
      },
      "source": [
        "sns.countplot(x=\"Behavior\",data=mice)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe24715de90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmn4dO3rpThm"
      },
      "source": [
        "Memantine    570\n",
        "\n",
        "Saline       510"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "8rK6WC1YkeKo",
        "outputId": "5e2ce341-b49d-4ea7-909d-f72509c6f722"
      },
      "source": [
        "sns.countplot(x=\"Treatment\",data=mice)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe246c22c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ufa3tQuAvqurYqrp4llp/BPhpBmNZvSfJfkn+JfBm4KSqOhbYBLxtJ/9baM/1JeCoJP8nyUeS/JvW/qH2GXwJcACDz9u2HAjc0D6zXwHe0dovAS6uqpcDbwI+uuv/CXs/u4/2AFV1c5KlDM4Srt5i8WuANyR5Z5t/LvBDbfq6qnoUeDTJw8AXWvstDMIFBl1TvwscAhzE4DmRGZ+vqu8Dt898GxvB/2jfBJ9MsgFYBJwCHA/c2L4EHgBsGHF/2ktU1WNJjgdexeALzCfaEPmPJnkX8DzgMOA2Nn9WZ/MUMHNNbS2DLlWAnwSOGTrRODjJQVX12K79l+zdDIU9x2rg/cDJwOFD7QHeVFVPGxiwXcB7cqjp+0Pz32fz//vLgVOr6ptJzm77nzG8/aijcA1vs6kdJ8Cqqvr1EfehvVRVbQKuB65Pcgvwiwy+oExV1d1JLmLwxWZbvlebH7Ca+YzBoOfjxKp6YpcXPo/YfbTn+BjwW1V1yxbtfw6cP9MPm+S4Hdzv84F7k+zHaF06j7ZtdsS1wGlJXgiQ5LAkWx2lUXunJP8iydFDTccCM19m7k9yEHDaM7cc2ZeA84eOd+yz2Ne85ZnCHqKq7gFmu/Xzd4A/BG5Osg/wbbbfJzvsvzC4RjHd3rf3B/9mYFOSbzI4y/jG9g5QVbcneTfwpVbj94DzgLt2oE7t+Q4CPpjkEGAjsI7BMPgPAbcC9zEYA21n/TLw4SQ3M/jb9hXg3z+riuchh7mQJHV2H0mSOkNBktQZCpKkzlCQJHWGgiSp85ZUzTtJDmfw7ATADzB4AGq6zZ9QVU/txD5PBp6qqr/aJUVO+DiavwwFzTtV9QCDB6doT9A+VlXvn1meZEFVbdzB3Z4MPAaM+4/1XB1H85TPKWhemwkFBoMHPgEcB/wl8OH2Wgg8Dryjqv42yeuBdzMY8vkBBk+BHwDcwOYzjvMZDCz4T21/LwR+gcEghK8Avl5VZ7fjvwb4LWB/4B+Ac9oYQXcCq4DXA/sBp7f6nnacqvqLsfyH0bzlNQVpsyXAK6vqAgY/FH9+VR0PvBP4SFvnqwzG1zmOwei176qqO4E/YjBC57FDf6gPZRACv8Zg7KqLgX8F/GiSY9vvCbwb+MmqehmD36W4YKie+1v7pcA7t3EcaZex+0ja7FNVtamNwfNK4FNDI27u396XMBjd80gGZwvf3sb+vlBV1QZ++87MuFVJbmPwY0VLgGOAv2zHeQ6DYc1nfLa9r2UwBLo0doaCtNk/tvd9gIfabz9s6YPAH1TV6nbR96Jt7G94VNotR6xdwKAb6Jqqest2th8eCVQaK7uPpC1U1SPAt5OcDpCBl7bFL2Dzz6EuH9psZ0aPvQE4KcmL23EOTPLD29lmZ44jjcxQkGb3NuDcNhrsbcCy1n4Rg26ltcD9Q+t/Afi37edGXzXKAapqGjgb+Hgb2fNrDH65blt2+DjSjvDuI0lS55mCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpO7/A5jL3f0+VxNtAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "fm0gkYhNkg60",
        "outputId": "846a50ac-ef7f-4433-ac71-04eb70b7c6a3"
      },
      "source": [
        "mice['Genotype'].value_counts().plot(kind='pie',autopct='%1.1f%%');\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "7Ilu6myukmNp",
        "outputId": "a89158e9-ef41-4ef1-f21d-21b850b2ea5b"
      },
      "source": [
        "mice['class'].value_counts().plot(kind='pie',autopct='%1.1f%%');\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "JkxDmYKhkpv-",
        "outputId": "00c20aec-2ef5-4046-ea4a-1db9d8360f86"
      },
      "source": [
        "ax=nmice[\"DYRK1A_N\"].plot.hist()\n",
        "ax.set_xlabel(\"DYRK1A_N\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'DYRK1A_N')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "8EBrJ_WUkso4",
        "outputId": "1f71cb64-c4f5-42c1-be2e-33de9d1e57b5"
      },
      "source": [
        "ax=nmice[\"pPKCG_N\"].plot.hist()\n",
        "ax.set_xlabel(\"pPKCG_N\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'pPKCG_N')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "8C3wmL7ckw-r",
        "outputId": "a570823c-89c3-4a72-a66a-685cb8278e07"
      },
      "source": [
        "ax=nmice[\"SOD1_N\"].plot.hist()\n",
        "ax.set_xlabel(\"SOD1_N\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'SOD1_N')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "La2JXBm0qp6q",
        "outputId": "dc9d9144-a2a4-42b2-f897-2622ed6f337e"
      },
      "source": [
        "ax=nmice[\"CaNA_N\"].plot.hist()\n",
        "ax.set_xlabel(\"SOD1_N\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'SOD1_N')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "h2nqyEEcrEqr",
        "outputId": "b076a1ad-2ca9-4749-b137-4a4e73156ed5"
      },
      "source": [
        "ax=nmice[\"Ubiquitin_N\"].plot.hist()\n",
        "ax.set_xlabel(\"Ubiquitin_N\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Ubiquitin_N')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "de-hrvo1Kko3",
        "outputId": "0ba7db77-1730-435a-bbb0-aea7ceed8cf1"
      },
      "source": [
        "sns.set(rc={\"figure.figsize\": (10,7)})\n",
        "ax = sns.distplot(nmice.loc[:,'DYRK1A_N':'pERK_N'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "EaYpzRsOKndK",
        "outputId": "ecdca04a-5769-4c51-a185-c8104e9e174a"
      },
      "source": [
        "ax = sns.distplot(nmice.loc[:,'pJNK_N':'BRAF_N'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSAvC9y8AYBp"
      },
      "source": [
        "**DIVIDING THE DATASET FOR TRAINING AND TESTING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ac6dnRuXAjhL"
      },
      "source": [
        "X = nmice.drop(['class','Genotype','Treatment','Behavior','MouseID'],axis=1)\n",
        "y = nmice['class']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "7CCIyRmIK7pq",
        "outputId": "eb485e8e-eec4-4edc-a10d-72c705b3155d"
      },
      "source": [
        "ax = sns.distplot(X)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZYuOx9ctHNh"
      },
      "source": [
        "**THE PROTEIN VALUES IN THE DATASET ARE NORMALLY DISTRIBUTED**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6pKC21mhUTdm"
      },
      "source": [
        "**IDENTIFICATION OF THE MOST CONTRIBUTING PROTEINS TO THE OUTCOME**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYIKLB6JUSFl",
        "outputId": "21eab502-651c-4ccc-879c-f55ddfd41917"
      },
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import f_classif\n",
        "# instantiate SelectKBest to determine 20 best features\n",
        "best_features = SelectKBest(score_func=f_classif, k=20)\n",
        "fit = best_features.fit(X,y)\n",
        "df_scores = pd.DataFrame(fit.scores_)\n",
        "df_columns = pd.DataFrame(X.columns)\n",
        "# concatenate dataframes\n",
        "feature_scores = pd.concat([df_columns, df_scores],axis=1)\n",
        "feature_scores.columns = ['Feature_Name','Score']  # name output columns\n",
        "print(feature_scores.nlargest(20,'Score'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   Feature_Name       Score\n",
            "32       SOD1_N  292.756964\n",
            "76       CaNA_N  240.971768\n",
            "65  Ubiquitin_N  161.163800\n",
            "53        ARC_N  138.893732\n",
            "70        pS6_N  138.893732\n",
            "34        P38_N  134.793298\n",
            "48         S6_N   98.047339\n",
            "17     pPKCAB_N   96.209139\n",
            "45     pGSK3B_N   86.619666\n",
            "10       pERK_N   85.536800\n",
            "35      pMTOR_N   85.346501\n",
            "64       SNCA_N   76.785852\n",
            "7     pCAMKII_N   74.735241\n",
            "30        APP_N   74.514174\n",
            "60       IL1B_N   71.075500\n",
            "15      pNR2A_N   68.604215\n",
            "39      pNUMB_N   66.942333\n",
            "0      DYRK1A_N   62.154393\n",
            "1       ITSN1_N   61.768682\n",
            "33       MTOR_N   57.625014\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T0lHurFpVhBq"
      },
      "source": [
        "THE PROTEINS CRITICAL TO LEARNING ABILITY ARE AS FOLLOWS\n",
        "\n",
        "\n",
        "  SOD1_N\n",
        "\n",
        "  CaNA_N \n",
        "\n",
        "  Ubiquitin_N  \n",
        "        ARC_N  \n",
        "        pS6_N  \n",
        "        P38_N  \n",
        "         S6_N   \n",
        "     pPKCAB_N   \n",
        "     pGSK3B_N   \n",
        "       pERK_N   \n",
        "      pMTOR_N   \n",
        "       SNCA_N   \n",
        "    pCAMKII_N   \n",
        "        APP_N   \n",
        "       IL1B_N   \n",
        "      pNR2A_N   \n",
        "      pNUMB_N   \n",
        "     DYRK1A_N   \n",
        "      ITSN1_N   \n",
        "       MTOR_N\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SeHbXC31E4d7"
      },
      "source": [
        "**TRAIN-TEST SPLITTING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYTg_3GOE5NF"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.35)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcSYhwfQBgYG"
      },
      "source": [
        "**DATA STANDARDIZATION**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMqGtWNxA4xK"
      },
      "source": [
        "**STANDARDIZING THE NUMERICAL ATTRIBUTES**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H0ZdWh5IA5jJ"
      },
      "source": [
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmxOMg7OD_xA"
      },
      "source": [
        "**MODEL BUILDING**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XKKHaqAsEW-5"
      },
      "source": [
        "**K-NEAREST NEIGHBOR -- BEFORE HYPERPARAMETER TUNING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wuTjQTQ0JoM",
        "outputId": "1e911916-f119-4a30-9546-162c51f89fad"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix\n",
        "model_b= KNeighborsClassifier(n_neighbors = 6, metric = 'minkowski', p = 2)\n",
        "model_b.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bfm-TH4jufyF"
      },
      "source": [
        "**MODEL EVALUATION**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8BXR8H7-c5fz"
      },
      "source": [
        "def multiclass_roc_auc_score(y_test, y_pred2, average=\"macro\"):\n",
        "  lb = preprocessing.LabelBinarizer()\n",
        "  lb.fit(y_test)\n",
        "  lb.fit(y_pred2)\n",
        "  y_test = lb.transform(y_test)\n",
        "  y_pred2 = lb.transform(y_pred2)\n",
        "  return roc_auc_score(y_test, y_pred2, average=average)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "shIuBYjNua7N",
        "outputId": "99a1b589-f22e-4919-e4a0-7db67b353ccb"
      },
      "source": [
        "y_pred = model_b.predict(X_test)\n",
        "kfold = model_selection.KFold(n_splits=10, random_state = 0)\n",
        "result = model_selection.cross_val_score(model_b, X_train, y_train, cv=kfold)\n",
        "print('Accuracy of k-NN Model = ',result.mean())\n",
        "f1_knn = f1_score(y_test, y_pred,average='macro')\n",
        "print('f1 score:',f1_knn)\n",
        "print('confusion matrix for 8  classes:\\n',confusion_matrix(y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of k-NN Model =  0.8690945674044267\n",
            "f1 score: 0.874367010868508\n",
            "confusion matrix for 8  classes:\n",
            " [[41  0  0  0  3  3  0  3]\n",
            " [ 9 34  1  0  0  1  0  0]\n",
            " [ 0  0 61  0  0  0  1  0]\n",
            " [ 0  0  0 46  0  0  1  0]\n",
            " [ 2  0  0  0 39  0  0  0]\n",
            " [ 2  2  0  0  4 28  0  0]\n",
            " [ 0  0  4  5  0  0 43  0]\n",
            " [ 1  0  2  1  0  0  1 40]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jf6TrVEAdDWj",
        "outputId": "7fbff518-38f2-4dd4-defa-21eb1a561449"
      },
      "source": [
        "print(\"roc score:\")\n",
        "multiclass_roc_auc_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "roc score:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9277070362980061"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_7qG-QwEiUX"
      },
      "source": [
        "**HYPER PARAMETER TUNING USING GRIDSEARCH CV**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XHhK55ApEAcG",
        "outputId": "f2612b4d-6138-44fa-8f05-25430bdb07f6"
      },
      "source": [
        "#making the instance\n",
        "model = KNeighborsClassifier()\n",
        "#Hyper Parameters Set\n",
        "params = {'n_neighbors':[5,6,7,8,9,10],\n",
        "          'leaf_size':[1,2,3,5,6,7,8,9,10],\n",
        "          'weights':['uniform', 'distance'],\n",
        "          'algorithm':['auto', 'ball_tree','kd_tree','brute'],\n",
        "          'n_jobs':[-1,1,2]}\n",
        "#Making model with hyper parameters sets\n",
        "model1 = GridSearchCV(model, param_grid=params, n_jobs=1)\n",
        "#Learning\n",
        "model1.fit(X_train,y_train)\n",
        "#The best hyper parameters set\n",
        "print(\"Best Hyper Parameters:\\n\",model1.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Hyper Parameters:\n",
            " {'algorithm': 'auto', 'leaf_size': 1, 'n_jobs': -1, 'n_neighbors': 6, 'weights': 'distance'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95thcDYYJY-w"
      },
      "source": [
        "**KNN MODEL WITH BEST HYPER PARAMETERS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2TpF62o9JXBv",
        "outputId": "c5c1fe31-5714-4660-a124-5bf2a6ba80a4"
      },
      "source": [
        "model_a= KNeighborsClassifier(n_neighbors = 6, metric = 'minkowski',leaf_size=1,n_jobs=-1,weights='distance')\n",
        "model_a.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=1, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=-1, n_neighbors=6, p=2,\n",
              "                     weights='distance')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T5LVH1iZKsbA"
      },
      "source": [
        "**PREDICTION AND EVALUATION USING K FOLD CROSS VALIDATION**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9W3dcYpJ2FA",
        "outputId": "a393c8bf-3c82-4b02-d87c-afe39ce2e228"
      },
      "source": [
        "y_pred1 = model_a.predict(X_test)\n",
        "kfold = model_selection.KFold(n_splits=10)\n",
        "result = model_selection.cross_val_score(model_a, X_train, y_train, cv=kfold)\n",
        "y_pred1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['c-CS-s', 't-CS-s', 'c-SC-s', 'c-SC-m', 'c-CS-s', 'c-CS-s',\n",
              "       't-SC-m', 't-CS-s', 'c-CS-s', 'c-SC-s', 'c-SC-m', 't-SC-m',\n",
              "       'c-CS-m', 'c-CS-m', 'c-CS-s', 'c-CS-s', 'c-CS-s', 't-SC-m',\n",
              "       'c-SC-m', 'c-CS-s', 'c-CS-m', 'c-CS-s', 'c-SC-m', 't-SC-m',\n",
              "       't-CS-s', 'c-CS-s', 'c-CS-s', 't-SC-m', 't-CS-s', 't-SC-s',\n",
              "       't-SC-s', 't-CS-m', 't-CS-s', 't-SC-m', 't-CS-m', 't-SC-s',\n",
              "       't-SC-s', 't-SC-s', 'c-CS-s', 'c-CS-m', 't-CS-m', 't-SC-s',\n",
              "       't-SC-m', 'c-CS-s', 't-SC-s', 'c-SC-m', 't-SC-m', 't-SC-m',\n",
              "       't-CS-m', 't-SC-s', 't-CS-s', 'c-SC-m', 'c-CS-s', 't-CS-m',\n",
              "       'c-CS-s', 'c-SC-m', 'c-SC-s', 't-CS-m', 't-SC-s', 't-SC-m',\n",
              "       't-SC-s', 'c-CS-m', 'c-SC-s', 'c-SC-s', 't-SC-m', 'c-CS-m',\n",
              "       't-CS-m', 't-SC-m', 't-CS-m', 'c-CS-m', 'c-CS-m', 'c-SC-s',\n",
              "       'c-SC-m', 'c-SC-m', 't-SC-m', 'c-SC-s', 't-CS-s', 'c-SC-s',\n",
              "       't-SC-m', 't-CS-s', 't-CS-m', 't-SC-m', 't-SC-s', 't-SC-m',\n",
              "       'c-SC-m', 'c-CS-s', 't-SC-s', 'c-SC-m', 't-CS-m', 'c-SC-s',\n",
              "       't-SC-s', 'c-SC-m', 'c-SC-s', 'c-CS-m', 'c-SC-s', 't-CS-s',\n",
              "       't-SC-s', 'c-SC-s', 'c-CS-s', 't-SC-s', 't-SC-m', 'c-SC-s',\n",
              "       'c-CS-m', 'c-SC-m', 't-CS-s', 'c-SC-m', 'c-SC-m', 'c-CS-m',\n",
              "       't-CS-m', 'c-CS-s', 'c-CS-m', 't-SC-s', 't-CS-m', 'c-CS-m',\n",
              "       't-SC-s', 'c-CS-m', 'c-SC-s', 'c-SC-s', 'c-CS-s', 't-SC-m',\n",
              "       't-CS-s', 'c-CS-s', 't-CS-m', 'c-CS-m', 'c-SC-s', 'c-CS-m',\n",
              "       't-SC-m', 'c-CS-m', 't-CS-m', 't-CS-m', 't-SC-s', 'c-CS-m',\n",
              "       't-SC-m', 'c-SC-m', 'c-SC-m', 't-CS-s', 't-CS-m', 't-SC-s',\n",
              "       't-CS-m', 'c-SC-m', 't-SC-s', 'c-SC-m', 't-SC-m', 'c-CS-m',\n",
              "       't-CS-s', 'c-CS-s', 'c-SC-s', 't-CS-s', 'c-CS-m', 't-SC-s',\n",
              "       'c-SC-m', 'c-SC-m', 'c-CS-s', 't-SC-s', 't-SC-s', 't-CS-m',\n",
              "       'c-SC-m', 't-SC-m', 'c-SC-m', 't-CS-m', 'c-SC-m', 'c-CS-m',\n",
              "       'c-SC-m', 'c-CS-s', 'c-SC-s', 'c-CS-s', 't-SC-s', 't-SC-s',\n",
              "       'c-SC-s', 'c-SC-m', 't-CS-m', 'c-SC-m', 'c-SC-s', 't-SC-m',\n",
              "       'c-SC-s', 'c-CS-s', 'c-CS-m', 't-SC-m', 't-SC-s', 'c-CS-s',\n",
              "       't-SC-s', 'c-CS-m', 't-SC-s', 't-SC-s', 'c-CS-s', 't-CS-s',\n",
              "       'c-SC-m', 't-CS-m', 'c-SC-m', 'c-SC-m', 'c-SC-s', 'c-SC-s',\n",
              "       't-SC-m', 't-SC-m', 't-SC-m', 't-CS-m', 'c-SC-s', 'c-SC-m',\n",
              "       'c-CS-s', 'c-SC-m', 'c-SC-s', 'c-SC-m', 't-SC-s', 'c-SC-s',\n",
              "       't-SC-m', 'c-CS-s', 'c-CS-s', 't-CS-s', 'c-SC-s', 't-CS-s',\n",
              "       'c-CS-s', 't-SC-m', 't-CS-m', 't-CS-s', 'c-CS-m', 't-CS-s',\n",
              "       't-SC-m', 'c-CS-m', 't-SC-m', 't-SC-s', 'c-SC-m', 't-CS-s',\n",
              "       'c-SC-m', 'c-SC-m', 't-SC-m', 't-SC-m', 't-CS-m', 't-CS-m',\n",
              "       'c-SC-s', 'c-SC-s', 't-CS-s', 'c-CS-m', 't-CS-m', 't-SC-m',\n",
              "       'c-CS-m', 'c-SC-s', 't-CS-s', 't-CS-m', 'c-SC-m', 't-CS-s',\n",
              "       'c-SC-s', 't-CS-s', 'c-CS-m', 't-SC-s', 't-SC-s', 'c-SC-m',\n",
              "       't-CS-s', 'c-SC-s', 't-CS-m', 'c-CS-m', 't-SC-m', 't-SC-s',\n",
              "       't-SC-m', 't-SC-s', 'c-CS-m', 't-CS-s', 'c-SC-m', 'c-CS-m',\n",
              "       't-CS-m', 't-CS-m', 'c-CS-s', 'c-CS-m', 'c-CS-m', 't-SC-s',\n",
              "       't-CS-m', 't-CS-m', 't-SC-m', 't-SC-m', 't-SC-m', 't-CS-m',\n",
              "       'c-SC-s', 'c-SC-m', 'c-CS-m', 't-CS-s', 'c-CS-s', 't-CS-m',\n",
              "       'c-SC-s', 't-SC-s', 't-SC-m', 't-CS-m', 'c-SC-m', 't-CS-m',\n",
              "       'c-SC-m', 'c-CS-s', 'c-CS-m', 't-CS-m', 't-CS-s', 'c-CS-s',\n",
              "       'c-SC-s', 'c-SC-m', 't-CS-m', 't-SC-m', 'c-SC-m', 'c-SC-s',\n",
              "       't-SC-s', 't-CS-s', 'c-CS-s', 'c-SC-s', 'c-CS-s', 'c-SC-s',\n",
              "       't-SC-s', 't-CS-s', 't-SC-m', 't-SC-m', 'c-SC-s', 'c-CS-m',\n",
              "       't-SC-s', 'c-SC-s', 'c-SC-m', 't-SC-m', 'c-SC-s', 'c-SC-s',\n",
              "       'c-SC-s', 't-CS-s', 'c-CS-m', 'c-SC-m', 'c-CS-s', 'c-SC-m',\n",
              "       'c-CS-s', 'c-CS-m', 't-SC-s', 'c-CS-m', 'c-CS-m', 'c-SC-m',\n",
              "       'c-CS-s', 't-CS-m', 'c-CS-m', 'c-CS-s', 'c-CS-m', 'c-CS-s',\n",
              "       'c-SC-m', 't-SC-m', 'c-CS-m', 'c-SC-s', 't-SC-s', 't-SC-m',\n",
              "       'c-SC-m', 't-CS-s', 'c-SC-m', 't-CS-m', 't-SC-m', 'c-SC-s',\n",
              "       'c-CS-s', 'c-CS-m', 'c-SC-m', 't-SC-s', 'c-SC-m', 't-CS-s',\n",
              "       'c-CS-m', 'c-CS-m', 't-CS-m', 'c-SC-s', 't-SC-m', 't-CS-s',\n",
              "       'c-CS-m', 'c-CS-m', 'c-SC-m', 'c-SC-m', 't-SC-m', 't-SC-m',\n",
              "       'c-CS-m', 't-SC-m', 'c-SC-m', 'c-SC-m', 't-CS-m', 't-SC-m',\n",
              "       't-CS-s', 't-SC-s', 't-SC-s', 'c-CS-s', 't-SC-s', 'c-SC-m',\n",
              "       'c-SC-m', 't-CS-s', 't-SC-m', 'c-CS-s', 't-SC-s', 'c-SC-s'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0oYawmcLC2e",
        "outputId": "0f3831f5-f39f-443b-dd7c-54caa4082c3e"
      },
      "source": [
        "print('Accuracy of k-NN Model = ',result.mean())\n",
        "f1_knn = f1_score(y_test, y_pred1,average='macro')\n",
        "print('f1 score:',f1_knn)\n",
        "print('confusion matrix for 8  classes:\\n',confusion_matrix(y_test,y_pred1))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of k-NN Model =  0.9572635814889336\n",
            "f1 score: 0.9851837817434077\n",
            "confusion matrix for 8  classes:\n",
            " [[49  0  0  0  1  0  0  0]\n",
            " [ 0 45  0  0  0  0  0  0]\n",
            " [ 0  0 59  1  0  0  1  1]\n",
            " [ 0  0  0 46  0  0  1  0]\n",
            " [ 0  0  0  0 41  0  0  0]\n",
            " [ 0  0  0  0  0 36  0  0]\n",
            " [ 0  0  0  0  0  0 51  1]\n",
            " [ 0  0  0  0  0  0  0 45]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4D4UNuuwhxZ0",
        "outputId": "45863672-836e-4a13-a908-4626299b6ff9"
      },
      "source": [
        "print(\"roc score:\")\n",
        "multiclass_roc_auc_score(y_test, y_pred1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "roc score:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9920610034895312"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lDi2gyzr3AL1"
      },
      "source": [
        "GIVEN THE PROTEIN EXPRESSION VALUES OF THE SPECIFIED 77 PROTEINS, THE K NEAREST NEIGHBOUR MODEL PREDICTS THE TYPE OF MOUSE WHETHER ITS GENOTYPE IS CONTROL OR TRISOMIC, TREATMENT GIVEN IS MEMANTINE OR SALINE AND BEHAVIOR IS CONTEXT SHOCK OR SHOCK CONTEXT WITH AN ACCURACY OF 96.44%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I0V5nFsORCrV"
      },
      "source": [
        "**PREDICTING FOR UNSEEN DATA**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BUttR4OyRIgb",
        "outputId": "56b13fa8-f050-45fc-b9b5-7e8fa0c34363"
      },
      "source": [
        "new_data1=[['0.503644','2.816329','5.990152','0.218830','0.177565','1.750936','0.687906','0.306382','0.402698','1.022060',\n",
        "            '1.877684','0.441599','0.859366','0.416289','1.866358','3.685247','0.165846','3.037621','0.369510','0.458539',\n",
        "            '0.335336','0.576916','0.448099','0.586271','0.394721','0.339571','0.482864','0.114783','0.131790','0.128186', \n",
        "            '1.443091','0.294700','0.354605','1.339070','0.188852','0.106305','0.144989','0.176668','0.125190','0.142756',\n",
        "             '0.430957','1.603310','2.014875','0.108234','0.134762','0.427099','0.114783','0.131790','0.128186','0.9964',\n",
        "            '0.503644','0.747193','0.430175','2.816329','5.990152','0.218830','0.177565','2.373744','0.232224','1.750936','0.687906',\n",
        "            '0.306382','0.402698','0.296927','1.022060','0.605673','1.877684','2.308745','0.441599','0.859366','0.416289','0.369608',\n",
        "            '0.178944','1.866358','3.685247','1.537227','0.264526'\t\n",
        "          ]]\n",
        "a=model_a.predict(new_data1)\n",
        "print(\"the given input data belongs to class:\",a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "the given input data belongs to class: ['t-SC-m']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:171: FutureWarning: Beginning in version 0.22, arrays of bytes/strings will be converted to decimal numbers if dtype='numeric'. It is recommended that you convert the array to a float dtype before using it in scikit-learn, for example by using your_array = your_array.astype(np.float64).\n",
            "  X = check_array(X, accept_sparse='csr')\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_base.py:605: FutureWarning: Beginning in version 0.22, arrays of bytes/strings will be converted to decimal numbers if dtype='numeric'. It is recommended that you convert the array to a float dtype before using it in scikit-learn, for example by using your_array = your_array.astype(np.float64).\n",
            "  X = check_array(X, accept_sparse='csr')\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-oDRyDu27O6"
      },
      "source": [
        "model1=LogisticRegression(C=10, class_weight=None, dual=False,\n",
        "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
        "          multi_class='multinomial', n_jobs=None, penalty='l2', random_state=None,\n",
        "          solver='newton-cg', tol=0.0001, verbose=0, warm_start=False)\n",
        "model1.fit(X_train, y_train)\n",
        "y_pred4 = model1.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78aFLvHi4Sdq"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model2 = RandomForestClassifier(n_estimators = 15, max_depth=None,criterion = 'entropy', random_state = 0)\n",
        "model2.fit(X_train, y_train)\n",
        "y_pred5 = model2.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DuKhVZruADtD"
      },
      "source": [
        "**VOTING ENSEMBLE CLASSIFIER**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RlQU2ZK1YZC"
      },
      "source": [
        "models = list()\n",
        "models.append(('knn',model_a))\n",
        "models.append(('lr',model1))\n",
        "models.append(('rf',model2))\n",
        "ensemble = VotingClassifier(estimators=models, voting='hard')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yTPclYv04p-M",
        "outputId": "bccb8bd2-11b3-4b08-fd5f-0fda1416dba9"
      },
      "source": [
        "ensemble.fit(X_train, y_train)\n",
        "y_pred6 = ensemble.predict(X_test)\n",
        "y_pred6"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['t-CS-m', 't-SC-m', 'c-CS-s', 't-SC-m', 't-SC-s', 't-SC-m',\n",
              "       'c-CS-m', 't-SC-m', 't-CS-m', 't-CS-m', 'c-CS-s', 'c-SC-m',\n",
              "       't-SC-m', 't-SC-m', 'c-SC-s', 'c-SC-s', 'c-SC-s', 't-SC-m',\n",
              "       't-CS-s', 'c-SC-m', 'c-SC-m', 't-SC-s', 't-CS-s', 't-CS-m',\n",
              "       't-SC-s', 't-CS-s', 'c-CS-s', 't-CS-s', 'c-CS-m', 't-CS-m',\n",
              "       'c-CS-m', 't-SC-m', 'c-SC-m', 't-CS-m', 't-SC-s', 'c-CS-m',\n",
              "       't-CS-m', 't-SC-s', 't-CS-m', 'c-SC-s', 't-SC-m', 't-CS-m',\n",
              "       'c-CS-s', 'c-CS-s', 'c-CS-s', 't-SC-s', 'c-SC-m', 't-CS-s',\n",
              "       'c-CS-s', 'c-CS-s', 't-CS-m', 't-CS-m', 't-SC-s', 't-CS-m',\n",
              "       'c-SC-m', 't-CS-s', 't-SC-s', 't-SC-s', 't-CS-m', 'c-CS-s',\n",
              "       'c-SC-m', 'c-CS-s', 't-SC-s', 'c-CS-m', 't-CS-m', 'c-CS-m',\n",
              "       't-CS-s', 't-CS-s', 'c-CS-m', 't-SC-s', 'c-SC-s', 't-CS-m',\n",
              "       'c-CS-s', 't-SC-s', 't-CS-s', 'c-SC-m', 't-SC-m', 'c-CS-m',\n",
              "       'c-SC-s', 'c-SC-m', 't-CS-s', 'c-CS-m', 'c-CS-s', 't-SC-m',\n",
              "       't-CS-m', 't-SC-m', 't-CS-s', 'c-SC-m', 'c-CS-m', 'c-CS-s',\n",
              "       'c-CS-s', 'c-SC-s', 'c-CS-m', 'c-SC-s', 'c-CS-s', 'c-CS-s',\n",
              "       'c-CS-m', 't-SC-s', 'c-CS-s', 'c-SC-s', 't-SC-m', 't-CS-m',\n",
              "       'c-CS-m', 't-CS-m', 't-CS-s', 't-CS-m', 'c-CS-s', 't-SC-s',\n",
              "       't-CS-s', 'c-CS-m', 'c-CS-m', 'c-CS-s', 'c-CS-m', 'c-SC-s',\n",
              "       'c-CS-m', 'c-CS-m', 't-SC-s', 't-SC-m', 't-SC-s', 't-SC-s',\n",
              "       'c-CS-s', 't-SC-s', 'c-CS-s', 'c-SC-m', 'c-SC-s', 't-SC-m',\n",
              "       'c-SC-m', 't-CS-s', 'c-SC-s', 't-CS-s', 'c-CS-s', 't-CS-s',\n",
              "       'c-CS-m', 'c-SC-m', 't-SC-s', 'c-SC-m', 'c-SC-s', 'c-SC-s',\n",
              "       'c-CS-s', 'c-SC-m', 't-CS-s', 'c-CS-m', 't-CS-m', 't-CS-s',\n",
              "       'c-CS-s', 'c-CS-s', 'c-SC-m', 't-CS-s', 't-SC-m', 't-SC-m',\n",
              "       'c-SC-s', 't-CS-s', 'c-CS-m', 't-CS-s', 't-CS-m', 'c-CS-s',\n",
              "       'c-CS-s', 'c-SC-s', 't-CS-m', 't-CS-m', 't-CS-s', 'c-SC-m',\n",
              "       't-SC-m', 't-SC-m', 't-CS-m', 'c-SC-m', 'c-CS-m', 'c-CS-s',\n",
              "       'c-CS-s', 'c-SC-m', 't-SC-m', 'c-SC-s', 't-SC-s', 't-SC-m',\n",
              "       't-SC-s', 't-CS-m', 'c-CS-m', 'c-CS-m', 't-CS-s', 't-CS-s',\n",
              "       'c-SC-s', 'c-CS-m', 'c-CS-m', 'c-CS-m', 'c-CS-s', 't-SC-m',\n",
              "       't-SC-s', 'c-SC-m', 't-CS-m', 't-SC-s', 't-SC-m', 'c-CS-s',\n",
              "       't-CS-m', 'c-SC-s', 't-SC-s', 'c-SC-s', 'c-CS-m', 't-SC-m',\n",
              "       't-CS-m', 't-CS-m', 't-SC-s', 'c-SC-s', 'c-CS-s', 'c-CS-m',\n",
              "       'c-CS-m', 't-CS-s', 'c-CS-s', 't-SC-s', 'c-SC-s', 't-CS-s',\n",
              "       'c-SC-m', 't-SC-m', 't-SC-s', 't-CS-s', 'c-CS-s', 't-CS-m',\n",
              "       'c-SC-s', 't-SC-m', 't-CS-m', 't-CS-s', 'c-CS-s', 'c-SC-s',\n",
              "       'c-SC-s', 't-CS-m', 'c-CS-s', 't-CS-m', 'c-SC-s', 't-SC-s',\n",
              "       't-SC-s', 't-SC-m', 't-SC-m', 't-SC-m', 'c-SC-m', 'c-SC-m',\n",
              "       't-CS-s', 'c-SC-m', 't-SC-m', 'c-SC-s', 't-SC-s', 'c-CS-m',\n",
              "       't-SC-m', 't-SC-s', 'c-SC-s', 'c-SC-m', 'c-SC-s', 't-SC-s',\n",
              "       't-SC-s', 'c-CS-m', 'c-CS-m', 'c-CS-s', 'c-SC-m', 'c-CS-m',\n",
              "       'c-CS-m', 't-CS-m', 't-SC-s', 'c-CS-s', 't-SC-s', 't-SC-m',\n",
              "       't-CS-m', 't-SC-m', 'c-SC-m', 't-SC-s', 'c-CS-m', 'c-SC-m',\n",
              "       'c-CS-m', 'c-SC-m', 'c-SC-s', 'c-SC-s', 't-CS-m', 't-SC-s',\n",
              "       'c-SC-s', 'c-SC-m', 'c-CS-m', 't-SC-m', 't-SC-s', 'c-CS-s',\n",
              "       'c-SC-m', 'c-SC-m', 'c-CS-m', 't-SC-s', 't-CS-m', 'c-SC-m',\n",
              "       'c-CS-m', 'c-SC-s', 't-SC-m', 'c-CS-m', 'c-SC-s', 'c-SC-s',\n",
              "       't-SC-s', 'c-SC-m', 'c-CS-m', 'c-SC-s', 'c-CS-m', 't-SC-m',\n",
              "       'c-SC-s', 't-SC-m', 't-CS-s', 't-CS-s', 'c-SC-s', 'c-SC-s',\n",
              "       'c-SC-s', 't-CS-m', 't-SC-m', 't-CS-m', 't-CS-m', 'c-CS-m',\n",
              "       'c-CS-m', 't-CS-s', 'c-CS-m', 't-CS-s', 'c-SC-m', 'c-SC-m',\n",
              "       't-SC-m', 'c-CS-m', 't-CS-m', 'c-SC-m', 't-SC-m', 't-SC-s',\n",
              "       'c-SC-s', 't-CS-m', 't-SC-m', 't-CS-m', 't-CS-m', 'c-SC-m',\n",
              "       'c-SC-s', 'c-CS-m', 't-SC-m', 't-CS-s', 'c-CS-m', 'c-CS-s',\n",
              "       'c-SC-s', 't-CS-s', 'c-CS-m', 't-CS-m', 'c-CS-m', 't-CS-m',\n",
              "       'c-SC-m', 'c-SC-m', 'c-SC-s', 'c-CS-s', 'c-SC-s', 't-CS-s',\n",
              "       't-SC-s', 'c-CS-m', 't-CS-m', 't-CS-s', 'c-SC-m', 't-CS-m',\n",
              "       't-SC-m', 'c-CS-m', 't-CS-s', 't-CS-m', 'c-CS-s', 't-CS-m',\n",
              "       'c-SC-s', 'c-SC-m', 'c-SC-s', 'c-CS-s', 'c-SC-s', 't-CS-s',\n",
              "       't-CS-m', 't-SC-m', 't-SC-s', 't-SC-m', 't-SC-m', 't-CS-s',\n",
              "       't-SC-s', 'c-CS-m', 'c-CS-s', 't-SC-m', 't-SC-m', 'c-CS-m',\n",
              "       't-CS-m', 't-SC-s', 'c-SC-m', 't-SC-m', 'c-SC-s', 'c-CS-m'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DsH7nxl353Yw",
        "outputId": "8e1dad09-c13b-4ac4-b91f-27ec5fb2c138"
      },
      "source": [
        "kfold = model_selection.KFold(n_splits=10)\n",
        "results = model_selection.cross_val_score(ensemble, X_train, y_train, cv=kfold)\n",
        "print('accuracy score of voting classifier:',results.mean())\n",
        "f1_VC = f1_score(y_test, y_pred6,average='macro')\n",
        "print('f1 score:',f1_VC)\n",
        "print('confusion matrix for 8  classes:\\n',confusion_matrix(y_test,y_pred6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy score of voting classifier: 0.9829376257545273\n",
            "f1 score: 0.9901004997520144\n",
            "confusion matrix for 8  classes:\n",
            " [[55  0  0  0  2  0  0  0]\n",
            " [ 1 44  0  0  0  0  0  0]\n",
            " [ 0  0 43  1  0  0  0  0]\n",
            " [ 0  0  0 48  0  0  0  0]\n",
            " [ 0  0  0  0 50  0  0  0]\n",
            " [ 0  0  0  0  0 40  0  0]\n",
            " [ 0  0  0  0  0  0 49  0]\n",
            " [ 0  0  0  0  0  0  0 45]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6Sm_9e_KaEo",
        "outputId": "6dd0e431-3698-4879-de45-a5b30a3eab43"
      },
      "source": [
        "print(\"roc score:\")\n",
        "multiclass_roc_auc_score(y_test, y_pred6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "roc score:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9942324785593024"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    }
  ]
}