notebooks/I hacked you.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"../datasets/ml_team_form.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_percentage = data['Έχω συμμετάσχει σε % συναντήσεις'].to_list()\n",
    "new_percentage = []\n",
    "for row in col_percentage:\n",
    "    row = row.replace(\"<100%\",\"80\")\n",
    "    row = row.replace(\"%\",\"\")\n",
    "    row = row.replace(\"<\",\"\")\n",
    "    new_percentage.append(int(row))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Έχω συμμετάσχει σε % συναντήσεις'] = new_percentage\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list_drop = []\n",
    "# for column in data.columns:\n",
    "#     if data[column].dtype == 'object':\n",
    "#         list_drop.append(column)\n",
    "# data = data.drop(list_drop,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['Timestamp','Ο λόγος που μπήκα στην ομάδα είναι για να:','Με δυσκολεύει κυρίως'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "object_column = []\n",
    "for column in data.columns:\n",
    "    if data[column].dtype == 'object':\n",
    "        object_column.append(column)\n",
    "for column in object_column:\n",
    "    new_list = []\n",
    "    list_to_consider = data[column].to_list()\n",
    "    for item in list_to_consider:\n",
    "        if item != 0:\n",
    "            new_list.append(1)\n",
    "        else:\n",
    "            new_list.append(0)\n",
    "    data[column] = new_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "X = data.drop(\"Θα συνεχίσω να συμμετέχω στις συναντήσεις\",axis=1)\n",
    "y = data['Θα συνεχίσω να συμμετέχω στις συναντήσεις'].to_list()\n",
    "\n",
    "train_X = X.iloc[:25]\n",
    "test_X = X.iloc[25:]\n",
    "train_y = y[:25]\n",
    "test_y = y[25:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = DecisionTreeClassifier(random_state=42).fit(train_X,train_y)\n",
    "predictions = classifier.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(predictions)\n",
    "print(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score,accuracy_score\n",
    "\n",
    "print(f1_score(test_y,predictions,average='macro'))\n",
    "print(accuracy_score(test_y,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"../datasets/ml_team_form_only_num.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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