diff --git a/models/Dementia Prediction Model/model.py b/models/Dementia Prediction Model/model.py
new file mode 100644
index 00000000..d18b6a0b
--- /dev/null
+++ b/models/Dementia Prediction Model/model.py
@@ -0,0 +1,31 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+import joblib
+
+class CarPriceModel:
+ def __init__(self):
+ self.model = LinearRegression()
+
+ def load_data(self, filepath):
+ data = pd.read_csv(filepath)
+ return data
+
+ def preprocess_data(self, data):
+ # Assuming 'price' is the target column and the rest are features
+ X = data.drop('price', axis=1) # Replace 'price' with the actual target column name
+ y = data['price'] # Replace 'price' with the actual target column name
+ return train_test_split(X, y, test_size=0.2, random_state=42)
+
+ def train(self, X_train, y_train):
+ self.model.fit(X_train, y_train)
+
+ def save_model(self, model_path):
+ joblib.dump(self.model, model_path)
+
+if __name__ == "__main__":
+ car_model = CarPriceModel()
+ data = car_model.load_data('data/cleaned_car_data.csv') # Adjust the path to your dataset
+ X_train, X_test, y_train, y_test = car_model.preprocess_data(data)
+ car_model.train(X_train, y_train)
+ car_model.save_model('saved_models/car_price_model.pkl')
\ No newline at end of file
diff --git a/models/Dementia Prediction Model/modelevalution.py b/models/Dementia Prediction Model/modelevalution.py
new file mode 100644
index 00000000..08e0b032
--- /dev/null
+++ b/models/Dementia Prediction Model/modelevalution.py
@@ -0,0 +1,21 @@
+import joblib
+import pandas as pd
+from sklearn.metrics import mean_squared_error, r2_score
+
+class ModelEvaluator:
+ def __init__(self, model_path):
+ self.model = joblib.load(model_path)
+
+ def evaluate(self, X_test, y_test):
+ predictions = self.model.predict(X_test)
+ mse = mean_squared_error(y_test, predictions)
+ r2 = r2_score(y_test, predictions)
+ print("Mean Squared Error:", mse)
+ print("R^2 Score:", r2)
+
+if __name__ == "__main__":
+ data = pd.read_csv('data/cleaned_car_data.csv') # Load your test data
+ X_test = data.drop('price', axis=1) # Replace 'price' with the actual target column name
+ y_test = data['price'] # Replace 'price' with the actual target column name
+ evaluator = ModelEvaluator('saved_models/car_price_model.pkl')
+ evaluator.evaluate(X_test, y_test)
\ No newline at end of file
diff --git a/models/Dementia Prediction Model/notebook/dementia-prediction-using-different-ml-model.ipynb b/models/Dementia Prediction Model/notebook/dementia-prediction-using-different-ml-model.ipynb
new file mode 100644
index 00000000..990a4df2
--- /dev/null
+++ b/models/Dementia Prediction Model/notebook/dementia-prediction-using-different-ml-model.ipynb
@@ -0,0 +1,3276 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.045864,
+ "end_time": "2021-02-21T05:24:40.532878",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:40.487014",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "## Importing Libs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+ "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:40.625317Z",
+ "iopub.status.busy": "2021-02-21T05:24:40.624657Z",
+ "iopub.status.idle": "2021-02-21T05:24:41.846215Z",
+ "shell.execute_reply": "2021-02-21T05:24:41.846810Z"
+ },
+ "papermill": {
+ "duration": 1.269347,
+ "end_time": "2021-02-21T05:24:41.847025",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:40.577678",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd # used to load, manipulate the data and for one-hot encoding\n",
+ "import numpy as np # data manipulation\n",
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.colors as colors\n",
+ "from sklearn.utils import resample # for downsample the dataset\n",
+ "from sklearn.model_selection import train_test_split # for splitting the dataset into train and test split\n",
+ "from sklearn.preprocessing import scale # scale and center the data\n",
+ "from sklearn.svm import SVC # will make a SVM for classification\n",
+ "from sklearn.model_selection import GridSearchCV # will do the cross validation\n",
+ "from sklearn.metrics import plot_confusion_matrix # will draw the confusion matrix\n",
+ "from sklearn.decomposition import PCA # to perform PCA to plot the data\n",
+ "from sklearn.impute import SimpleImputer\n",
+ "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+ "from sklearn.model_selection import cross_val_score\n",
+ "from sklearn.metrics import confusion_matrix, precision_score, accuracy_score, recall_score, roc_curve, auc\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.044613,
+ "end_time": "2021-02-21T05:24:41.936713",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:41.892100",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "## Load the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
+ "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:42.029211Z",
+ "iopub.status.busy": "2021-02-21T05:24:42.028542Z",
+ "iopub.status.idle": "2021-02-21T05:24:42.058687Z",
+ "shell.execute_reply": "2021-02-21T05:24:42.059215Z"
+ },
+ "papermill": {
+ "duration": 0.077355,
+ "end_time": "2021-02-21T05:24:42.059378",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:41.982023",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "data = pd.read_csv(\"../input/mri-and-alzheimers/oasis_longitudinal.csv\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.04491,
+ "end_time": "2021-02-21T05:24:42.149525",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:42.104615",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "## Explore the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:42.244342Z",
+ "iopub.status.busy": "2021-02-21T05:24:42.243655Z",
+ "iopub.status.idle": "2021-02-21T05:24:42.249012Z",
+ "shell.execute_reply": "2021-02-21T05:24:42.248425Z"
+ },
+ "papermill": {
+ "duration": 0.053142,
+ "end_time": "2021-02-21T05:24:42.249140",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:42.195998",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "pd.set_option('display.max_columns', None) # will show the all columns with pandas dataframe\n",
+ "pd.set_option('display.max_rows', None) # will show the all rows with pandas dataframe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:42.359739Z",
+ "iopub.status.busy": "2021-02-21T05:24:42.359068Z",
+ "iopub.status.idle": "2021-02-21T05:24:42.371999Z",
+ "shell.execute_reply": "2021-02-21T05:24:42.372499Z"
+ },
+ "papermill": {
+ "duration": 0.078228,
+ "end_time": "2021-02-21T05:24:42.372636",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:42.294408",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Subject ID | \n",
+ " MRI ID | \n",
+ " Group | \n",
+ " Visit | \n",
+ " MR Delay | \n",
+ " M/F | \n",
+ " Hand | \n",
+ " Age | \n",
+ " EDUC | \n",
+ " SES | \n",
+ " MMSE | \n",
+ " CDR | \n",
+ " eTIV | \n",
+ " nWBV | \n",
+ " ASF | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " OAS2_0001 | \n",
+ " OAS2_0001_MR1 | \n",
+ " Nondemented | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " M | \n",
+ " R | \n",
+ " 87 | \n",
+ " 14 | \n",
+ " 2.0 | \n",
+ " 27.0 | \n",
+ " 0.0 | \n",
+ " 1987 | \n",
+ " 0.696 | \n",
+ " 0.883 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " OAS2_0001 | \n",
+ " OAS2_0001_MR2 | \n",
+ " Nondemented | \n",
+ " 2 | \n",
+ " 457 | \n",
+ " M | \n",
+ " R | \n",
+ " 88 | \n",
+ " 14 | \n",
+ " 2.0 | \n",
+ " 30.0 | \n",
+ " 0.0 | \n",
+ " 2004 | \n",
+ " 0.681 | \n",
+ " 0.876 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " OAS2_0002 | \n",
+ " OAS2_0002_MR1 | \n",
+ " Demented | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " M | \n",
+ " R | \n",
+ " 75 | \n",
+ " 12 | \n",
+ " NaN | \n",
+ " 23.0 | \n",
+ " 0.5 | \n",
+ " 1678 | \n",
+ " 0.736 | \n",
+ " 1.046 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " OAS2_0002 | \n",
+ " OAS2_0002_MR2 | \n",
+ " Demented | \n",
+ " 2 | \n",
+ " 560 | \n",
+ " M | \n",
+ " R | \n",
+ " 76 | \n",
+ " 12 | \n",
+ " NaN | \n",
+ " 28.0 | \n",
+ " 0.5 | \n",
+ " 1738 | \n",
+ " 0.713 | \n",
+ " 1.010 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " OAS2_0002 | \n",
+ " OAS2_0002_MR3 | \n",
+ " Demented | \n",
+ " 3 | \n",
+ " 1895 | \n",
+ " M | \n",
+ " R | \n",
+ " 80 | \n",
+ " 12 | \n",
+ " NaN | \n",
+ " 22.0 | \n",
+ " 0.5 | \n",
+ " 1698 | \n",
+ " 0.701 | \n",
+ " 1.034 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from pandas.plotting import scatter_matrix\n",
+ "\n",
+ "attributes = [\"Group\", \"CDR\", \"M/F\", \"SES\", \"ASF\"]\n",
+ "\n",
+ "scatter_matrix(data[attributes], figsize=(15, 11), alpha=0.3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:45.433703Z",
+ "iopub.status.busy": "2021-02-21T05:24:45.433017Z",
+ "iopub.status.idle": "2021-02-21T05:24:47.540106Z",
+ "shell.execute_reply": "2021-02-21T05:24:47.540628Z"
+ },
+ "papermill": {
+ "duration": 2.163236,
+ "end_time": "2021-02-21T05:24:47.540769",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:45.377533",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import plotly.express as px\n",
+ "\n",
+ "fig = px.scatter(data, x='Group', y='SES', color='Group')\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:47.650865Z",
+ "iopub.status.busy": "2021-02-21T05:24:47.649810Z",
+ "iopub.status.idle": "2021-02-21T05:24:47.721817Z",
+ "shell.execute_reply": "2021-02-21T05:24:47.722413Z"
+ },
+ "papermill": {
+ "duration": 0.129299,
+ "end_time": "2021-02-21T05:24:47.722563",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:47.593264",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import plotly.express as px\n",
+ "\n",
+ "fig = px.scatter(data, x='Group', y='Age', color='Group')\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:47.832887Z",
+ "iopub.status.busy": "2021-02-21T05:24:47.831861Z",
+ "iopub.status.idle": "2021-02-21T05:24:47.904040Z",
+ "shell.execute_reply": "2021-02-21T05:24:47.904522Z"
+ },
+ "papermill": {
+ "duration": 0.130189,
+ "end_time": "2021-02-21T05:24:47.904674",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:47.774485",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import plotly.express as px\n",
+ "\n",
+ "fig = px.scatter(data, x='Group', y='ASF', color='Group')\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.053096,
+ "end_time": "2021-02-21T05:24:48.011350",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:47.958254",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "## Checking For Missig/Null Values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:48.126795Z",
+ "iopub.status.busy": "2021-02-21T05:24:48.125994Z",
+ "iopub.status.idle": "2021-02-21T05:24:48.130278Z",
+ "shell.execute_reply": "2021-02-21T05:24:48.129746Z"
+ },
+ "papermill": {
+ "duration": 0.065537,
+ "end_time": "2021-02-21T05:24:48.130397",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:48.064860",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Subject ID 0\n",
+ "MRI ID 0\n",
+ "Group 0\n",
+ "Visit 0\n",
+ "MR Delay 0\n",
+ "M/F 0\n",
+ "Hand 0\n",
+ "Age 0\n",
+ "EDUC 0\n",
+ "SES 19\n",
+ "MMSE 2\n",
+ "CDR 0\n",
+ "eTIV 0\n",
+ "nWBV 0\n",
+ "ASF 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.053518,
+ "end_time": "2021-02-21T05:24:48.237952",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:48.184434",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "### Taking median values for the missing values of MMSE"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:48.355837Z",
+ "iopub.status.busy": "2021-02-21T05:24:48.355128Z",
+ "iopub.status.idle": "2021-02-21T05:24:48.357901Z",
+ "shell.execute_reply": "2021-02-21T05:24:48.358483Z"
+ },
+ "papermill": {
+ "duration": 0.066666,
+ "end_time": "2021-02-21T05:24:48.358626",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:48.291960",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Subject ID 0\n",
+ "MRI ID 0\n",
+ "Group 0\n",
+ "Visit 0\n",
+ "MR Delay 0\n",
+ "M/F 0\n",
+ "Hand 0\n",
+ "Age 0\n",
+ "EDUC 0\n",
+ "SES 19\n",
+ "MMSE 0\n",
+ "CDR 0\n",
+ "eTIV 0\n",
+ "nWBV 0\n",
+ "ASF 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "median = data['MMSE'].median()\n",
+ "data['MMSE'].fillna(median, inplace=True)\n",
+ "data.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.054757,
+ "end_time": "2021-02-21T05:24:48.468036",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:48.413279",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "### Taking median values for the missing values of SES"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:48.580903Z",
+ "iopub.status.busy": "2021-02-21T05:24:48.580254Z",
+ "iopub.status.idle": "2021-02-21T05:24:48.589172Z",
+ "shell.execute_reply": "2021-02-21T05:24:48.589795Z"
+ },
+ "papermill": {
+ "duration": 0.067076,
+ "end_time": "2021-02-21T05:24:48.589954",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:48.522878",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Subject ID 0\n",
+ "MRI ID 0\n",
+ "Group 0\n",
+ "Visit 0\n",
+ "MR Delay 0\n",
+ "M/F 0\n",
+ "Hand 0\n",
+ "Age 0\n",
+ "EDUC 0\n",
+ "SES 0\n",
+ "MMSE 0\n",
+ "CDR 0\n",
+ "eTIV 0\n",
+ "nWBV 0\n",
+ "ASF 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "median = data['SES'].median()\n",
+ "data['SES'].fillna(median, inplace=True)\n",
+ "data.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.054489,
+ "end_time": "2021-02-21T05:24:48.700670",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:48.646181",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "## Train-Test Split"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.055984,
+ "end_time": "2021-02-21T05:24:48.812635",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:48.756651",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "## Prepare the data for X and y where, \n",
+ "\n",
+ "1. X = The columns/features for **making the prediction**\n",
+ "2. y = The **predicted value**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:48.926932Z",
+ "iopub.status.busy": "2021-02-21T05:24:48.926288Z",
+ "iopub.status.idle": "2021-02-21T05:24:48.933312Z",
+ "shell.execute_reply": "2021-02-21T05:24:48.932717Z"
+ },
+ "papermill": {
+ "duration": 0.065077,
+ "end_time": "2021-02-21T05:24:48.933436",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:48.868359",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "y = data['Group'].values\n",
+ "X = data[['M/F', 'Age', 'EDUC', 'SES', 'MMSE', 'eTIV', 'nWBV', 'ASF']]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.055011,
+ "end_time": "2021-02-21T05:24:49.044676",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:48.989665",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "### Train-Test distribution Without Stratified Sampling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:49.159712Z",
+ "iopub.status.busy": "2021-02-21T05:24:49.158704Z",
+ "iopub.status.idle": "2021-02-21T05:24:49.174698Z",
+ "shell.execute_reply": "2021-02-21T05:24:49.175197Z"
+ },
+ "papermill": {
+ "duration": 0.075266,
+ "end_time": "2021-02-21T05:24:49.175352",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:49.100086",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "In Training Split:\n",
+ "0 158\n",
+ "1 140\n",
+ "Name: 0, dtype: int64\n",
+ "\n",
+ "In Testing Split:\n",
+ "1 43\n",
+ "0 32\n",
+ "Name: 0, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "# by default test_size= 0.25\n",
+ "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size= 0.20, random_state=42)\n",
+ "\n",
+ "df_ytrain = pd.DataFrame(y_trainval)\n",
+ "df_ytest = pd.DataFrame(y_test)\n",
+ "\n",
+ "print('In Training Split:')\n",
+ "print(df_ytrain[0].value_counts())\n",
+ "\n",
+ "print('\\nIn Testing Split:')\n",
+ "print(df_ytest[0].value_counts())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.055463,
+ "end_time": "2021-02-21T05:24:49.286592",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:49.231129",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "### With Stratified Sampling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:49.402123Z",
+ "iopub.status.busy": "2021-02-21T05:24:49.401433Z",
+ "iopub.status.idle": "2021-02-21T05:24:49.418062Z",
+ "shell.execute_reply": "2021-02-21T05:24:49.417434Z"
+ },
+ "papermill": {
+ "duration": 0.075377,
+ "end_time": "2021-02-21T05:24:49.418177",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:49.342800",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "In Training Split:\n",
+ "0 152\n",
+ "1 146\n",
+ "Name: 0, dtype: int64\n",
+ "\n",
+ "In Testing Split:\n",
+ "0 38\n",
+ "1 37\n",
+ "Name: 0, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "# by default test_size= 0.25\n",
+ "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size= 0.20, random_state=42, stratify=y)\n",
+ "\n",
+ "\n",
+ "df_ytrain = pd.DataFrame(y_trainval)\n",
+ "df_ytest = pd.DataFrame(y_test)\n",
+ "\n",
+ "print('In Training Split:')\n",
+ "print(df_ytrain[0].value_counts())\n",
+ "\n",
+ "print('\\nIn Testing Split:')\n",
+ "print(df_ytest[0].value_counts())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.056065,
+ "end_time": "2021-02-21T05:24:49.530652",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:49.474587",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "### Scale the dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:49.651835Z",
+ "iopub.status.busy": "2021-02-21T05:24:49.650741Z",
+ "iopub.status.idle": "2021-02-21T05:24:49.660072Z",
+ "shell.execute_reply": "2021-02-21T05:24:49.659484Z"
+ },
+ "papermill": {
+ "duration": 0.073448,
+ "end_time": "2021-02-21T05:24:49.660201",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:49.586753",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# here StandardScaler() means z = (x - u) / s\n",
+ "scaler = StandardScaler().fit(X_trainval)\n",
+ "#scaler = MinMaxScaler().fit(X_trainval)\n",
+ "X_trainval_scaled = scaler.transform(X_trainval)\n",
+ "X_test_scaled = scaler.transform(X_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:49.777554Z",
+ "iopub.status.busy": "2021-02-21T05:24:49.776541Z",
+ "iopub.status.idle": "2021-02-21T05:24:49.782686Z",
+ "shell.execute_reply": "2021-02-21T05:24:49.783257Z"
+ },
+ "papermill": {
+ "duration": 0.066296,
+ "end_time": "2021-02-21T05:24:49.783400",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:49.717104",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[-0.87966444, 0.38449006, -0.87500081, ..., -0.30880564,\n",
+ " 0.16961408, 0.21548547],\n",
+ " [ 1.13679712, 1.04832574, -0.87500081, ..., 1.23805919,\n",
+ " -0.67676996, -1.21558429],\n",
+ " [-0.87966444, -0.9431813 , -1.22928103, ..., -1.08511327,\n",
+ " 0.4605586 , 1.14751551],\n",
+ " ...,\n",
+ " [-0.87966444, -0.01381135, 1.2506805 , ..., -0.92985174,\n",
+ " 0.01091708, 0.94202857],\n",
+ " [-0.87966444, -1.20871557, -0.87500081, ..., -0.00978344,\n",
+ " 0.5663566 , -0.10742258],\n",
+ " [-0.87966444, 0.11895579, 1.2506805 , ..., -1.38413547,\n",
+ " 0.4605586 , 1.56582821]])"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_trainval_scaled"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-02-21T05:24:49.907580Z",
+ "iopub.status.busy": "2021-02-21T05:24:49.906515Z",
+ "iopub.status.idle": "2021-02-21T05:24:49.938315Z",
+ "shell.execute_reply": "2021-02-21T05:24:49.937670Z"
+ },
+ "papermill": {
+ "duration": 0.09737,
+ "end_time": "2021-02-21T05:24:49.938439",
+ "exception": false,
+ "start_time": "2021-02-21T05:24:49.841069",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " M/F | \n",
+ " Age | \n",
+ " EDUC | \n",
+ " SES | \n",
+ " MMSE | \n",
+ " eTIV | \n",
+ " nWBV | \n",
+ " ASF | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | count | \n",
+ " 298.000000 | \n",
+ " 298.000000 | \n",
+ " 298.000000 | \n",
+ " 298.000000 | \n",
+ " 298.000000 | \n",
+ " 298.000000 | \n",
+ " 298.000000 | \n",
+ " 298.000000 | \n",
+ "
\n",
+ " \n",
+ " | mean | \n",
+ " 0.436242 | \n",
+ " 77.104027 | \n",
+ " 14.469799 | \n",
+ " 2.489933 | \n",
+ " 27.355705 | \n",
+ " 1483.701342 | \n",
+ " 0.730587 | \n",
+ " 1.198638 | \n",
+ "
\n",
+ " \n",
+ " | std | \n",
+ " 0.496752 | \n",
+ " 7.544654 | \n",
+ " 2.827372 | \n",
+ " 1.116859 | \n",
+ " 3.689231 | \n",
+ " 174.192649 | \n",
+ " 0.037871 | \n",
+ " 0.136491 | \n",
+ "
\n",
+ " \n",
+ " | min | \n",
+ " 0.000000 | \n",
+ " 60.000000 | \n",
+ " 6.000000 | \n",
+ " 1.000000 | \n",
+ " 4.000000 | \n",
+ " 1106.000000 | \n",
+ " 0.644000 | \n",
+ " 0.883000 | \n",
+ "
\n",
+ " \n",
+ " | 25% | \n",
+ " 0.000000 | \n",
+ " 71.250000 | \n",
+ " 12.000000 | \n",
+ " 2.000000 | \n",
+ " 27.000000 | \n",
+ " 1357.000000 | \n",
+ " 0.699250 | \n",
+ " 1.107250 | \n",
+ "
\n",
+ " \n",
+ " | 50% | \n",
+ " 0.000000 | \n",
+ " 77.000000 | \n",
+ " 14.000000 | \n",
+ " 2.000000 | \n",
+ " 29.000000 | \n",
+ " 1462.000000 | \n",
+ " 0.731000 | \n",
+ " 1.200500 | \n",
+ "
\n",
+ " \n",
+ " | 75% | \n",
+ " 1.000000 | \n",
+ " 82.000000 | \n",
+ " 16.000000 | \n",
+ " 3.000000 | \n",
+ " 30.000000 | \n",
+ " 1585.250000 | \n",
+ " 0.757000 | \n",
+ " 1.293000 | \n",
+ "
\n",
+ " \n",
+ " | max | \n",
+ " 1.000000 | \n",
+ " 96.000000 | \n",
+ " 23.000000 | \n",
+ " 5.000000 | \n",
+ " 30.000000 | \n",
+ " 1987.000000 | \n",
+ " 0.837000 | \n",
+ " 1.587000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "