1_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"color:green\" align=\"center\">Machine Learning With Python: Linear Regression With One Variable</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Problem Statement**: Given above data build a machine learning model that can predict home prices based on square feet area\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>725000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area   price\n",
       "0  2600  550000\n",
       "1  3000  565000\n",
       "2  3200  610000\n",
       "3  3600  680000\n",
       "4  4000  725000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =  pd.read_csv('homeprices.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type of price <class 'pandas.core.series.Series'>\n",
      "type of dataframe <class 'pandas.core.frame.DataFrame'>\n",
      "shape of data frame (5, 2)\n",
      "shape of price (5,)\n"
     ]
    }
   ],
   "source": [
    "pricedata = df[\"price\"]\n",
    "print('type of price',type(pricedata))\n",
    "print('type of dataframe',type(df))\n",
    "print('shape of data frame',df.shape)\n",
    "print('shape of price',pricedata.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x260e4a6e990>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('area')\n",
    "plt.ylabel('price')\n",
    "plt.scatter(df.area,df.price,color='red',marker='*')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df.area))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.drop('price',axis='columns')\n",
    "new_df\n",
    "type(new_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = df.price\n",
    "price\n",
    "type(price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9584301138199486"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create linear regression object\n",
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(new_df,price)\n",
    "reg.score(new_df,price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x260e4b46450>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('area')\n",
    "plt.ylabel('price')\n",
    "plt.scatter(df.area,df.price,color='red',marker='*')\n",
    "plt.plot(new_df,reg.predict(new_df),color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(1) Predict price of a home with area = 3300 sqr ft**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([615136.98630137])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[3200]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([628715.75342466])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[3300]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([135.78767123])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180616.43835616432"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Y = m * X + b (m is coefficient and b is intercept)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "628715.7534151643"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3300*135.78767123 + 180616.43835616432"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(1) Predict price of a home with area = 5000 sqr ft**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([859554.79452055])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[5000]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"color:purple\">Generate CSV file with list of home price predictions</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area\n",
       "0  1000\n",
       "1  1500\n",
       "2  2300"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_df = pd.read_csv(\"areas.csv\")\n",
    "area_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = reg.predict(area_df)\n",
    "p\n",
    "\n",
    "reg.score(area_df,p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>3.164041e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500</td>\n",
       "      <td>3.842979e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2300</td>\n",
       "      <td>4.929281e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3540</td>\n",
       "      <td>6.613048e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4120</td>\n",
       "      <td>7.400616e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4560</td>\n",
       "      <td>7.998082e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5490</td>\n",
       "      <td>9.260908e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3460</td>\n",
       "      <td>6.504418e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4750</td>\n",
       "      <td>8.256079e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2300</td>\n",
       "      <td>4.929281e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9000</td>\n",
       "      <td>1.402705e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8600</td>\n",
       "      <td>1.348390e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7100</td>\n",
       "      <td>1.144709e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    area        prices\n",
       "0   1000  3.164041e+05\n",
       "1   1500  3.842979e+05\n",
       "2   2300  4.929281e+05\n",
       "3   3540  6.613048e+05\n",
       "4   4120  7.400616e+05\n",
       "5   4560  7.998082e+05\n",
       "6   5490  9.260908e+05\n",
       "7   3460  6.504418e+05\n",
       "8   4750  8.256079e+05\n",
       "9   2300  4.929281e+05\n",
       "10  9000  1.402705e+06\n",
       "11  8600  1.348390e+06\n",
       "12  7100  1.144709e+06"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_df['prices']=p\n",
    "area_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_df.to_csv(\"predictionMarch24.csv\")"
   ]
  }
 ],
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