changed save modle tutorial

Author: dhavalsays

ML/4_save_model/4_save_and_load_model_using_pickle.ipynb

@@ -9,10 +9,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -22,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -89,7 +87,7 @@
        "4  4000  725000"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -101,16 +99,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 3,
    "metadata": {},
    "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": 62,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -122,16 +121,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 135.78767123])"
+       "array([135.78767123])"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -142,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -151,7 +150,7 @@
        "180616.43835616432"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -162,22 +161,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 859554.79452055])"
+       "array([859554.79452055])"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model.predict(5000)"
+    "model.predict([[5000]])"
    ]
   },
   {
@@ -189,18 +188,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pickle"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,10 +214,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [],
    "source": [
     "with open('model_pickle','rb') as file:\n",
@@ -229,16 +224,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 135.78767123])"
+       "array([135.78767123])"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -249,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 12,
    "metadata": {
     "scrolled": true
    },
@@ -260,7 +255,7 @@
        "180616.43835616432"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -271,22 +266,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 859554.79452055])"
+       "array([859554.79452055])"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mp.predict(5000)"
+    "mp.predict([[5000]])"
    ]
   },
   {
@@ -298,18 +293,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.externals import joblib"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -318,7 +311,7 @@
        "['model_joblib']"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -336,27 +329,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [],
    "source": [
     "mj = joblib.load('model_joblib')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 135.78767123])"
+       "array([135.78767123])"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -367,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 18,
    "metadata": {
     "scrolled": true
    },
@@ -378,7 +369,7 @@
        "180616.43835616432"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -389,22 +380,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 859554.79452055])"
+       "array([859554.79452055])"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mj.predict(5000)"
+    "mj.predict([[5000]])"
    ]
   }
  ],
@@ -424,7 +415,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,