01. Linear Regression .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression\n",
    "\n",
    "**y= mx +c**\n",
    "\n",
    "**here \"y\" is dependent variable and \"x\" is independent variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"/Users/sudhanshukumar/Documents/Machine Learning/csv files/homeprices.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 5,
     "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": [
    "#we have kept price on y axis as is it is target variable\n",
    "plt.scatter(df.area,df.price,color=\"r\",marker=\"*\") \n",
    "plt.xlabel(\"area\")\n",
    "plt.ylabel(\"price\")\n",
    "\n",
    "#from the graph we can see the that the ditribution is suitable for linear regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=linear_model.LinearRegression()  # creating object for linear regresion\n",
    "\n",
    "#this step is knows as training linear regression model(object)\n",
    "\n",
    "#reg.fit(x(independent),y(dependent))\n",
    "reg.fit(df[[\"area\"]] ,df.price)    #2d array is expected here so \"[[]]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[628715.75342466] \n",
      "\n",
      "[859554.79452055]\n"
     ]
    }
   ],
   "source": [
    "print(reg.predict([[3300]]),\"\\n\")\n",
    "print(reg.predict([[5000]]))  #here basically the value of 'm' and 'c' is calculated from the equation y=mx+c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**the model basically calculates the value of 'm' (slope) and 'c' (intercept) which remains constant. These values are used in the equation y=mx+c and then the different values of y is caluclating depending on upon x(independent)**"
   ]
  },
  {
   "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_      #value of 'm' or slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180616.43835616432"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_    #value of 'c' or intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price for area 3300:  628715.7534151643 \n",
      "\n",
      "price for area 5000:  859554.7945061643 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#  y=mx+c   knowing the mechanism of the program. value of 'x' is 'area'(independent variable)\n",
    "\n",
    "y=135.78767123*3300+180616.43835616432\n",
    "print(\"price for area 3300: \",y,\"\\n\")\n",
    "\n",
    "y=135.78767123*5000+180616.43835616432\n",
    "print(\"price for area 5000: \",y,\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    area\n",
       "0   1000\n",
       "1   1500\n",
       "2   2300\n",
       "3   3540\n",
       "4   4120\n",
       "5   4560\n",
       "6   5490\n",
       "7   3460\n",
       "8   4750\n",
       "9   2300\n",
       "10  9000\n",
       "11  8600\n",
       "12  7100"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"/Users/sudhanshukumar/Documents/Machine Learning/csv files/areas.csv\")\n",
    "print(type(data))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 316404.10958904,  384297.94520548,  492928.08219178,\n",
       "        661304.79452055,  740061.64383562,  799808.21917808,\n",
       "        926090.75342466,  650441.78082192,  825607.87671233,\n",
       "        492928.08219178, 1402705.47945205, 1348390.4109589 ,\n",
       "       1144708.90410959])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr=reg.predict(data)  #here the same reg object is used as its trained(by the previous data)\n",
    "pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": 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": [
    "data[\"prices\"]=pr   #this creates a new column in the data frame\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"predictions\")    #converts to csv and saves in the same directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[[\"area\"]])   #since 2d array--> 2 brackets are used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f900fff8510>]"
      ]
     },
     "execution_count": 34,
     "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": [
    "#our graph\n",
    "\n",
    "plt.scatter(df.area,df.price,color=\"r\",marker=\"*\") \n",
    "plt.xlabel(\"area\")\n",
    "plt.ylabel(\"price\")\n",
    "\n",
    "plt.plot(df.area,reg.predict(df[[\"area\"]]),color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area\n",
       "0  2600\n",
       "1  3000\n",
       "2  3200\n",
       "3  3600\n",
       "4  4000"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df.drop(\"price\",axis=1)\n",
    "print(type(X))\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    550000\n",
       "1    565000\n",
       "2    610000\n",
       "3    680000\n",
       "4    725000\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.price\n",
    "print(type(y))\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9584301138199486"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X,y)   #model score\n",
    "                 #value for x-axis must be a data frame\n",
    "                 #value for y-axis can be both "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}