Aish_Assignment_1_linreg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Name: Aishwarya Vivek Bhavsar\n",
    "CSULB ID: 029371509\n",
    "CECS 551 Assignment 1 - Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MedInc',\n",
      " 'HouseAge',\n",
      " 'AveRooms',\n",
      " 'AveBedrms',\n",
      " 'Population',\n",
      " 'AveOccup',\n",
      " 'Latitude',\n",
      " 'Longitude']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from pprint import pprint\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from math import sqrt\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "raw = fetch_california_housing()\n",
    "vectorizer = TfidfVectorizer()\n",
    "\n",
    "X = raw.data\n",
    "y = raw.target\n",
    "# Show feature names\n",
    "pprint(list(raw.feature_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _california_housing_dataset:\n",
      "\n",
      "California Housing dataset\n",
      "--------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 20640\n",
      "\n",
      "    :Number of Attributes: 8 numeric, predictive attributes and the target\n",
      "\n",
      "    :Attribute Information:\n",
      "        - MedInc        median income in block\n",
      "        - HouseAge      median house age in block\n",
      "        - AveRooms      average number of rooms\n",
      "        - AveBedrms     average number of bedrooms\n",
      "        - Population    block population\n",
      "        - AveOccup      average house occupancy\n",
      "        - Latitude      house block latitude\n",
      "        - Longitude     house block longitude\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "This dataset was obtained from the StatLib repository.\n",
      "http://lib.stat.cmu.edu/datasets/\n",
      "\n",
      "The target variable is the median house value for California districts.\n",
      "\n",
      "This dataset was derived from the 1990 U.S. census, using one row per census\n",
      "block group. A block group is the smallest geographical unit for which the U.S.\n",
      "Census Bureau publishes sample data (a block group typically has a population\n",
      "of 600 to 3,000 people).\n",
      "\n",
      "It can be downloaded/loaded using the\n",
      ":func:`sklearn.datasets.fetch_california_housing` function.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
      "      Statistics and Probability Letters, 33 (1997) 291-297\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Show dataset description\n",
    "print(raw.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20640, 8)\n"
     ]
    }
   ],
   "source": [
    "# Show dimension of X\n",
    "print(raw.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20640,)\n"
     ]
    }
   ],
   "source": [
    "# Show dimension of y\n",
    "print(raw.target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split X, y into X_train, X_test, y_train, y_test with 7:3 ratio\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
    "\n",
    "#t = max(X_train)\n",
    "#print(t)\n",
    "#type(X_train)\n",
    "#print(X_train.ndim)\n",
    "#[X_train[0] for i in X_train]\n",
    "\n",
    "#print(\"TRAINING\" , X_train)\n",
    "#print(\"TESTING\", X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6139951151748542\n"
     ]
    }
   ],
   "source": [
    "# Build a linear regression model with X_train, y_train\n",
    "reg = LinearRegression()      #impose a linear regression. \n",
    "reg.fit(X_train, y_train)     #this will shape the model using one predictor. We use fit() to train our model.\n",
    "print(reg.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.11532804 1.54082385 1.89245758 ... 2.23750302 2.31435565 0.81096116]\n",
      "0.725707380639667\n",
      "0.6139951151748542\n"
     ]
    }
   ],
   "source": [
    "# pridict y_pred from X_test\n",
    "#predict() uses the learned parameters from fit() and performs predictions on the unseen, test datapoints on each instance.\n",
    "y_pred = reg.predict(np.array(X_test))  \n",
    "\n",
    "print(y_pred)\n",
    "print(sqrt(mean_squared_error(y_test,y_pred)))\n",
    "print((r2_score(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.35999973e-01,  9.19351259e-03, -1.11365790e-01,  6.11671735e-01,\n",
       "       -5.10567461e-06, -4.16210012e-03, -4.17361064e-01, -4.28305550e-01])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show coefficients of the features and find the argmax of coefficients\n",
    "#print(\"array(\",reg.coef_,\")\")\n",
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "#find argmax of coefficients\n",
    "p = np.argmax(reg.coef_, axis = None, out = None)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.0, 15.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw scatter plots of \n",
    "#             max coefficient feature of X_train - y_train as 'x' marker\n",
    "#             max coefficient feature of X_test - y_test as 'o' marker\n",
    "# xlim(2,10)\n",
    "x = X_train[:,3]\n",
    "y = X_test[:,3]\n",
    "plt.scatter(x,y_train,marker='x')\n",
    "plt.scatter(y,y_test)\n",
    "plt.xlim(2,15)"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}