01 - Linear Regression.py.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn import linear_model\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy:  0.8561711884626345\n",
      "Coefficient:  [ 0.14915438  0.97858197 -0.18705036 -0.25739415  0.03514419]\n",
      "Intercept:  -1.4132846299271602\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"student-mat.csv\", sep=\";\")\n",
    "data = data[[\"G1\", \"G2\", \"G3\", \"studytime\", \"failures\", \"absences\"]]\n",
    "\n",
    "predict = \"G3\"\n",
    "\n",
    "x = np.array(data.drop([predict], 1))\n",
    "y = np.array(data[predict])\n",
    "\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.1)\n",
    "\n",
    "linear = linear_model.LinearRegression()\n",
    "\n",
    "linear.fit(x_train, y_train)\n",
    "\n",
    "accuracy = linear.score(x_test,y_test)\n",
    "print(\"Model Accuracy: \", accuracy)\n",
    "\n",
    "print(\"Coefficient: \", linear.coef_) #We have a 5 dimensional space here so we have 5 Coefficients! (our line starting position depending on 5 axis )\n",
    "print(\"Intercept: \", linear.intercept_) # y\n",
    "\n",
    "\n",
    "with open(\"studentGradeModel.pickle\", \"wb\") as modelFile:\n",
    "    pickle.dump(linear, modelFile)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}