updated linear regression multi variable tutorial

Author: dhavalsays

ML/2_linear_reg_multivariate/2_linear_regression_multivariate.ipynb

@@ -62,10 +62,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -75,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 2,
    "metadata": {
     "scrolled": true
    },
@@ -164,7 +162,7 @@
        "5  4100       6.0    8  810000"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -183,30 +181,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import math\n",
-    "med_bedrooms = math.floor(df.bedrooms.median())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "df.bedrooms = df.bedrooms.fillna(med_bedrooms)"
+    "df.bedrooms.median()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -293,50 +288,52 @@
        "5  4100       6.0    8  810000"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "df.bedrooms = df.bedrooms.fillna(df.bedrooms.median())\n",
     "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
+       "         normalize=False)"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "reg = linear_model.LinearRegression()\n",
-    "reg.fit(df[['area','bedrooms','age']],df.price)"
+    "reg.fit(df.drop('price',axis='columns'),df.price)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([   112.06244194,  23388.88007794,  -3231.71790863])"
+       "array([  112.06244194, 23388.88007794, -3231.71790863])"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -347,18 +344,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 8,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "221323.00186540384"
+       "221323.00186540408"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -376,16 +373,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 498408.25158031])"
+       "array([498408.25158031])"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -396,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 10,
    "metadata": {
     "scrolled": true
    },
@@ -407,7 +404,7 @@
        "498408.25157402386"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -425,16 +422,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 578876.03748933])"
+       "array([578876.03748933])"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -493,7 +490,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,