mock_1_a.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e53302b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age  salary  experience  target\n",
      "0   25   50000           2       1\n",
      "1   32   60000           5       0\n",
      "2   47   80000          10       1\n",
      "3   51  120000          15       0\n",
      "4   62  100000          20       1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Task 1\n",
    "\n",
    "# Subtask 1\n",
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load and print data\n",
    "data = pd.read_csv(r\"C:\\Users\\abrao\\Documents\\Archive\\data.csv\")\n",
    "print(data.head(5))\n",
    "\n",
    "# Subtask 2\n",
    "# Histogram of age distribution\n",
    "plt.hist(data['age'], color='teal')\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5302075c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Task 2\n",
    "\n",
    "# Subtask 1\n",
    "x = data.drop('target', axis=1)\n",
    "y = data['target']\n",
    "\n",
    "# Subtask 2\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "scaler = StandardScaler()\n",
    "x_scaled = scaler.fit_transform(x)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_scaled, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a76f2ca3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          Model       MSE\n",
      "0             Linear Regression  0.195393\n",
      "1       Random Forest Regressor  0.286100\n",
      "2  Gradient Boosting Regression  0.499107\n",
      "old score: 0.28609999999999997. new score: 0.25311151464852605\n"
     ]
    }
   ],
   "source": [
    "# Task 3\n",
    "\n",
    "# Subtask 1\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "\n",
    "# Subtask 2\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "linear_rgs = LinearRegression().fit(x_train, y_train)\n",
    "linear_rgs_y_pred = linear_rgs.predict(x_test)\n",
    "linear_rgs_mse = mean_squared_error(y_test, linear_rgs_y_pred)\n",
    "\n",
    "randon_forest_rgs = RandomForestRegressor().fit(x_train, y_train)\n",
    "randon_forest_rgs_y_pred = randon_forest_rgs.predict(x_test)\n",
    "randon_forest_rgs_mse = mean_squared_error(y_test, randon_forest_rgs_y_pred)\n",
    "\n",
    "gradient_boostin_rgs = GradientBoostingRegressor().fit(x_train, y_train)\n",
    "gradient_boostin_rgs_y_pred = gradient_boostin_rgs.predict(x_test)\n",
    "gradient_boostin_rgs_mse = mean_squared_error(y_test, gradient_boostin_rgs_y_pred)\n",
    "\n",
    "data = {\n",
    "    'Model': ['Linear Regression', 'Random Forest Regressor', 'Gradient Boosting Regression'],\n",
    "    'MSE': [linear_rgs_mse, randon_forest_rgs_mse, gradient_boostin_rgs_mse]\n",
    "}\n",
    "\n",
    "print(pd.DataFrame(data))\n",
    "\n",
    "# Subtask 3\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators' : [1, 10, 100, 1000],\n",
    "    'min_samples_split': [2, 3, 4, 5],\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=RandomForestRegressor(), param_grid=param_grid, cv=2)\n",
    "grid_search.fit(x_train, y_train)\n",
    "grid_search_y_pred = grid_search.predict(x_test)\n",
    "grid_search_mse = mean_squared_error(y_test, grid_search_y_pred)\n",
    "\n",
    "print(f'old score: {randon_forest_rgs_mse}. new score: {grid_search_mse}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}