06-LINEAR REGRESSION.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀\n",
    "</br>\n",
    "In this notebook, I'm going to talk about linear regression.\n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mglearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w[0]: 0.393906  b: -0.031804\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_linear_regression_wave()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=mglearn.datasets.make_wave(n_samples=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_egitim, X_test, y_egitim,y_test=train_test_split(X,y,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression().fit(X_egitim,y_egitim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.39390555]\n"
     ]
    }
   ],
   "source": [
    "print(lr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.031804343026759746\n"
     ]
    }
   ],
   "source": [
    "print(lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6700890315075756\n"
     ]
    }
   ],
   "source": [
    "print(lr.score(X_egitim,y_egitim))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.65933685968637\n"
     ]
    }
   ],
   "source": [
    "print(lr.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Impoting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston=load_boston()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(boston['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=mglearn.datasets.load_extended_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 104)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_egitim,X_test,y_egitim,y_test=train_test_split(X,y,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression().fit(X_egitim,y_egitim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9520519609032727\n",
      "0.6074721959665925\n"
     ]
    }
   ],
   "source": [
    "print(lr.score(X_egitim,y_egitim))\n",
    "print(lr.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
 ],
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