google developers club hackathon.ipynb

{
  "cells": [
    {
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "# importing Libraries\nimport numpy as np\nimport pandas as pd\nimport xgboost as xgb\nimport seaborn as sns\nimport matplotlib.pylab as plt\n\nfrom sklearn.model_selection import train_test_split\nfrom pylab import plot, show, subplot, specgram, imshow, savefig\nfrom sklearn import preprocessing\nfrom sklearn import cross_validation, metrics\nfrom sklearn.preprocessing import Normalizer\nfrom sklearn.cross_validation import cross_val_score\nfrom sklearn.preprocessing import Imputer\n\nimport matplotlib.pyplot as plote\n\n%matplotlib inline\n\nplt.style.use('ggplot')\n",
      "execution_count": 63,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "train = pd.read_csv('../input/GDSChackathon.csv')",
      "execution_count": 64,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "7536d88b-1951-41f3-9d1c-019886dd146e",
        "_uuid": "ccf8779c8142e8a44875055530ff107a7fa107a6",
        "trusted": true
      },
      "cell_type": "code",
      "source": "train.head()",
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 65,
          "data": {
            "text/plain": "   color         ...          movie_facebook_likes\n0  Color         ...                         33000\n1  Color         ...                             0\n2  Color         ...                         85000\n3  Color         ...                        164000\n4    NaN         ...                             0\n\n[5 rows x 28 columns]",
            "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>color</th>\n      <th>director_name</th>\n      <th>num_critic_for_reviews</th>\n      <th>duration</th>\n      <th>director_facebook_likes</th>\n      <th>actor_3_facebook_likes</th>\n      <th>actor_2_name</th>\n      <th>actor_1_facebook_likes</th>\n      <th>gross</th>\n      <th>genres</th>\n      <th>actor_1_name</th>\n      <th>movie_title</th>\n      <th>num_voted_users</th>\n      <th>cast_total_facebook_likes</th>\n      <th>actor_3_name</th>\n      <th>facenumber_in_poster</th>\n      <th>plot_keywords</th>\n      <th>movie_imdb_link</th>\n      <th>num_user_for_reviews</th>\n      <th>language</th>\n      <th>country</th>\n      <th>content_rating</th>\n      <th>budget</th>\n      <th>title_year</th>\n      <th>actor_2_facebook_likes</th>\n      <th>imdb_score</th>\n      <th>aspect_ratio</th>\n      <th>movie_facebook_likes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Color</td>\n      <td>James Cameron</td>\n      <td>723.0</td>\n      <td>178.0</td>\n      <td>0.0</td>\n      <td>855.0</td>\n      <td>Joel David Moore</td>\n      <td>1000.0</td>\n      <td>760505847.0</td>\n      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n      <td>CCH Pounder</td>\n      <td>Avatar</td>\n      <td>886204</td>\n      <td>4834</td>\n      <td>Wes Studi</td>\n      <td>0.0</td>\n      <td>avatar|future|marine|native|paraplegic</td>\n      <td>http://www.imdb.com/title/tt0499549/?ref_=fn_t...</td>\n      <td>3054.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>237000000.0</td>\n      <td>2009.0</td>\n      <td>936.0</td>\n      <td>7.9</td>\n      <td>1.78</td>\n      <td>33000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Color</td>\n      <td>Gore Verbinski</td>\n      <td>302.0</td>\n      <td>169.0</td>\n      <td>563.0</td>\n      <td>1000.0</td>\n      <td>Orlando Bloom</td>\n      <td>40000.0</td>\n      <td>309404152.0</td>\n      <td>Action|Adventure|Fantasy</td>\n      <td>Johnny Depp</td>\n      <td>Pirates of the Caribbean: At World's End</td>\n      <td>471220</td>\n      <td>48350</td>\n      <td>Jack Davenport</td>\n      <td>0.0</td>\n      <td>goddess|marriage ceremony|marriage proposal|pi...</td>\n      <td>http://www.imdb.com/title/tt0449088/?ref_=fn_t...</td>\n      <td>1238.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>300000000.0</td>\n      <td>2007.0</td>\n      <td>5000.0</td>\n      <td>7.1</td>\n      <td>2.35</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Color</td>\n      <td>Sam Mendes</td>\n      <td>602.0</td>\n      <td>148.0</td>\n      <td>0.0</td>\n      <td>161.0</td>\n      <td>Rory Kinnear</td>\n      <td>11000.0</td>\n      <td>200074175.0</td>\n      <td>Action|Adventure|Thriller</td>\n      <td>Christoph Waltz</td>\n      <td>Spectre</td>\n      <td>275868</td>\n      <td>11700</td>\n      <td>Stephanie Sigman</td>\n      <td>1.0</td>\n      <td>bomb|espionage|sequel|spy|terrorist</td>\n      <td>http://www.imdb.com/title/tt2379713/?ref_=fn_t...</td>\n      <td>994.0</td>\n      <td>English</td>\n      <td>UK</td>\n      <td>PG-13</td>\n      <td>245000000.0</td>\n      <td>2015.0</td>\n      <td>393.0</td>\n      <td>6.8</td>\n      <td>2.35</td>\n      <td>85000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Color</td>\n      <td>Christopher Nolan</td>\n      <td>813.0</td>\n      <td>164.0</td>\n      <td>22000.0</td>\n      <td>23000.0</td>\n      <td>Christian Bale</td>\n      <td>27000.0</td>\n      <td>448130642.0</td>\n      <td>Action|Thriller</td>\n      <td>Tom Hardy</td>\n      <td>The Dark Knight Rises</td>\n      <td>1144337</td>\n      <td>106759</td>\n      <td>Joseph Gordon-Levitt</td>\n      <td>0.0</td>\n      <td>deception|imprisonment|lawlessness|police offi...</td>\n      <td>http://www.imdb.com/title/tt1345836/?ref_=fn_t...</td>\n      <td>2701.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>250000000.0</td>\n      <td>2012.0</td>\n      <td>23000.0</td>\n      <td>8.5</td>\n      <td>2.35</td>\n      <td>164000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Doug Walker</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>131.0</td>\n      <td>NaN</td>\n      <td>Rob Walker</td>\n      <td>131.0</td>\n      <td>NaN</td>\n      <td>Documentary</td>\n      <td>Doug Walker</td>\n      <td>Star Wars: Episode VII - The Force Awakens    ...</td>\n      <td>8</td>\n      <td>143</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>http://www.imdb.com/title/tt5289954/?ref_=fn_t...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>7.1</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "a695724e-4bf2-49bd-8094-3d178d4b1808",
        "_uuid": "96d24bcb2c66c27f705ce7e98e026e092cda0c30",
        "trusted": true
      },
      "cell_type": "code",
      "source": "train.describe()",
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 66,
          "data": {
            "text/plain": "       num_critic_for_reviews          ...           movie_facebook_likes\ncount             4993.000000          ...                    5043.000000\nmean               140.194272          ...                    7525.964505\nstd                121.601675          ...                   19320.445110\nmin                  1.000000          ...                       0.000000\n25%                 50.000000          ...                       0.000000\n50%                110.000000          ...                     166.000000\n75%                195.000000          ...                    3000.000000\nmax                813.000000          ...                  349000.000000\n\n[8 rows x 16 columns]",
            "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>num_critic_for_reviews</th>\n      <th>duration</th>\n      <th>director_facebook_likes</th>\n      <th>actor_3_facebook_likes</th>\n      <th>actor_1_facebook_likes</th>\n      <th>gross</th>\n      <th>num_voted_users</th>\n      <th>cast_total_facebook_likes</th>\n      <th>facenumber_in_poster</th>\n      <th>num_user_for_reviews</th>\n      <th>budget</th>\n      <th>title_year</th>\n      <th>actor_2_facebook_likes</th>\n      <th>imdb_score</th>\n      <th>aspect_ratio</th>\n      <th>movie_facebook_likes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>4993.000000</td>\n      <td>5028.000000</td>\n      <td>4939.000000</td>\n      <td>5020.000000</td>\n      <td>5036.000000</td>\n      <td>4.159000e+03</td>\n      <td>5.043000e+03</td>\n      <td>5043.000000</td>\n      <td>5030.000000</td>\n      <td>5022.000000</td>\n      <td>4.551000e+03</td>\n      <td>4935.000000</td>\n      <td>5030.000000</td>\n      <td>5043.000000</td>\n      <td>4714.000000</td>\n      <td>5043.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>140.194272</td>\n      <td>107.201074</td>\n      <td>686.509212</td>\n      <td>645.009761</td>\n      <td>6560.047061</td>\n      <td>4.846841e+07</td>\n      <td>8.366816e+04</td>\n      <td>9699.063851</td>\n      <td>1.371173</td>\n      <td>272.770808</td>\n      <td>3.975262e+07</td>\n      <td>2002.470517</td>\n      <td>1651.754473</td>\n      <td>6.442138</td>\n      <td>2.220403</td>\n      <td>7525.964505</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>121.601675</td>\n      <td>25.197441</td>\n      <td>2813.328607</td>\n      <td>1665.041728</td>\n      <td>15020.759120</td>\n      <td>6.845299e+07</td>\n      <td>1.384853e+05</td>\n      <td>18163.799124</td>\n      <td>2.013576</td>\n      <td>377.982886</td>\n      <td>2.061149e+08</td>\n      <td>12.474599</td>\n      <td>4042.438863</td>\n      <td>1.125116</td>\n      <td>1.385113</td>\n      <td>19320.445110</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>7.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.620000e+02</td>\n      <td>5.000000e+00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>2.180000e+02</td>\n      <td>1916.000000</td>\n      <td>0.000000</td>\n      <td>1.600000</td>\n      <td>1.180000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>50.000000</td>\n      <td>93.000000</td>\n      <td>7.000000</td>\n      <td>133.000000</td>\n      <td>614.000000</td>\n      <td>5.340988e+06</td>\n      <td>8.593500e+03</td>\n      <td>1411.000000</td>\n      <td>0.000000</td>\n      <td>65.000000</td>\n      <td>6.000000e+06</td>\n      <td>1999.000000</td>\n      <td>281.000000</td>\n      <td>5.800000</td>\n      <td>1.850000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>110.000000</td>\n      <td>103.000000</td>\n      <td>49.000000</td>\n      <td>371.500000</td>\n      <td>988.000000</td>\n      <td>2.551750e+07</td>\n      <td>3.435900e+04</td>\n      <td>3090.000000</td>\n      <td>1.000000</td>\n      <td>156.000000</td>\n      <td>2.000000e+07</td>\n      <td>2005.000000</td>\n      <td>595.000000</td>\n      <td>6.600000</td>\n      <td>2.350000</td>\n      <td>166.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>195.000000</td>\n      <td>118.000000</td>\n      <td>194.500000</td>\n      <td>636.000000</td>\n      <td>11000.000000</td>\n      <td>6.230944e+07</td>\n      <td>9.630900e+04</td>\n      <td>13756.500000</td>\n      <td>2.000000</td>\n      <td>326.000000</td>\n      <td>4.500000e+07</td>\n      <td>2011.000000</td>\n      <td>918.000000</td>\n      <td>7.200000</td>\n      <td>2.350000</td>\n      <td>3000.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>813.000000</td>\n      <td>511.000000</td>\n      <td>23000.000000</td>\n      <td>23000.000000</td>\n      <td>640000.000000</td>\n      <td>7.605058e+08</td>\n      <td>1.689764e+06</td>\n      <td>656730.000000</td>\n      <td>43.000000</td>\n      <td>5060.000000</td>\n      <td>1.221550e+10</td>\n      <td>2016.000000</td>\n      <td>137000.000000</td>\n      <td>9.500000</td>\n      <td>16.000000</td>\n      <td>349000.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "e208da9d-0b9e-49dc-8f1f-bcc2055c45c3",
        "_uuid": "64ca733c85dfc32e54b71dc35c95cc62eff9f674"
      },
      "cell_type": "markdown",
      "source": "Finding types of datatypes"
    },
    {
      "metadata": {
        "_cell_guid": "534f32e0-a222-4ddb-815c-27483aae1a57",
        "_uuid": "4ee90e5ba2f417ffa68d2b9a9a5851d29fde4ed1",
        "trusted": true
      },
      "cell_type": "code",
      "source": "list(set(train.dtypes.tolist()))",
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 67,
          "data": {
            "text/plain": "[dtype('int64'), dtype('O'), dtype('float64')]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "7f91152b-a1c4-48f1-88e5-6601f75e59f3",
        "_uuid": "d3377f76d711b4bb09375cb1f3d8960fed20c721"
      },
      "cell_type": "markdown",
      "source": "## function for inding missing values in dataset"
    },
    {
      "metadata": {
        "_cell_guid": "c913cc8a-4447-4999-95e2-2c57e0f02675",
        "_uuid": "4721703f455b503c585dc4b685f27a99e85f2778",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# To check how many columns have missing values - this can be repeated to see the progress made\ndef show_missing():\n    missing = train.columns[train.isnull().any()].tolist()\n    return missing",
      "execution_count": 68,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "5e5dc873-c7cd-4a1b-8368-ae2d7ced23cb",
        "_uuid": "a426b34fd96d85614048434187f5087e69398ead"
      },
      "cell_type": "markdown",
      "source": "from this we can find the total missing data in each columns"
    },
    {
      "metadata": {
        "_cell_guid": "96f58a0a-1468-4b25-aff2-2acfa3423232",
        "_uuid": "9b5b6092cad3171605f53ff5fb20702cb21fad4e",
        "trusted": true
      },
      "cell_type": "code",
      "source": "train[show_missing()].isnull().sum()",
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 69,
          "data": {
            "text/plain": "color                       19\ndirector_name              104\nnum_critic_for_reviews      50\nduration                    15\ndirector_facebook_likes    104\nactor_3_facebook_likes      23\nactor_2_name                13\nactor_1_facebook_likes       7\ngross                      884\nactor_1_name                 7\nactor_3_name                23\nfacenumber_in_poster        13\nplot_keywords              153\nnum_user_for_reviews        21\nlanguage                    12\ncountry                      5\ncontent_rating             303\nbudget                     492\ntitle_year                 108\nactor_2_facebook_likes      13\naspect_ratio               329\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "e17be4ed-7159-4b16-8d14-94881f9a2606",
        "_uuid": "e79227a23a372077427366d45665886823b01278"
      },
      "cell_type": "markdown",
      "source": "## Checking is there any unespected value in score"
    },
    {
      "metadata": {
        "_cell_guid": "0b648a7e-908b-43fa-ac50-fd9afc6cc605",
        "_uuid": "682cb1335b799c13c2595db95789d052347fecfd",
        "trusted": true
      },
      "cell_type": "code",
      "source": "train['imdb_score'].describe()",
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 70,
          "data": {
            "text/plain": "count    5043.000000\nmean        6.442138\nstd         1.125116\nmin         1.600000\n25%         5.800000\n50%         6.600000\n75%         7.200000\nmax         9.500000\nName: imdb_score, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "d284a580-1243-4c9a-a64f-876490f58d47",
        "_uuid": "addc9ca6659b648df5ed6f5cac815e90668d2fa7"
      },
      "cell_type": "markdown",
      "source": "# Finding all categorical data"
    },
    {
      "metadata": {
        "_cell_guid": "20701a1a-8fbd-4c68-86f9-d5a308511eb5",
        "_uuid": "abf7095d252094391fc56a09b7fceb08633b3600",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "cats = []\nfor col in train.columns.values:\n    if train[col].dtype == 'object':\n        cats.append(col)\n",
      "execution_count": 71,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "9e332fcd-0382-4e91-adbc-fbbeda1b1da0",
        "_uuid": "bc49170555257006284296ecac634a568ab8d300"
      },
      "cell_type": "markdown",
      "source": "# Creating separte datasets for Continuous vs Categorical\n"
    },
    {
      "metadata": {
        "_cell_guid": "4b1cc381-e2b5-45c1-b450-a95ee9bd3268",
        "_uuid": "1ee3034fdadee526993a85ed56ec9ba2d4297b1d",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_cont = train.drop(cats, axis=1)\ndf_cat = train[cats]",
      "execution_count": 72,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "daa3c241-65b5-4466-9622-d3b4ac804f29",
        "_uuid": "3552523bb0061025e48a6059b2fa52859c512d3e"
      },
      "cell_type": "markdown",
      "source": "# Checking correlation\ncorrelation te;"
    },
    {
      "metadata": {
        "_cell_guid": "3403aaab-1676-45eb-a2d0-6be9e88468f4",
        "_uuid": "53171a7d28bf2bd11bb3e12c540d14410c3d6f05",
        "trusted": true
      },
      "cell_type": "code",
      "source": "corr=train.corr()[\"imdb_score\"]\ncorr[np.argsort(corr, axis=0)[::-1]]",
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 73,
          "data": {
            "text/plain": "imdb_score                   1.000000\nnum_voted_users              0.410965\nnum_critic_for_reviews       0.305303\nnum_user_for_reviews         0.292475\nduration                     0.261662\nmovie_facebook_likes         0.247049\ngross                        0.198021\ndirector_facebook_likes      0.170802\ncast_total_facebook_likes    0.085787\nactor_2_facebook_likes       0.083808\nactor_1_facebook_likes       0.076099\naspect_ratio                 0.059445\nactor_3_facebook_likes       0.052633\nbudget                       0.030688\nfacenumber_in_poster        -0.062958\ntitle_year                  -0.209167\nName: imdb_score, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "0172e85a-e54d-4358-82b1-db9c2a9218ca",
        "_uuid": "cc7db24aab30d77a4c14292d1a788e2041de857d"
      },
      "cell_type": "markdown",
      "source": "# Handle Missing Data for continuous data\n## If any column contains  entries of missing data, replace those missing values with the median\n"
    },
    {
      "metadata": {
        "_cell_guid": "3154261c-446c-4a80-8140-fab640ffcebf",
        "_uuid": "e1fbbc63c95112246dc410e75c2b7e35ed7db5c4",
        "trusted": true
      },
      "cell_type": "code",
      "source": "for col in df_cont.columns.values:\n      if np.sum(df_cont[col].isnull()) > 0:\n        median = df_cont[col].median()\n        idx = np.where(df_cont[col].isnull())[0]\n         \n        df_cont[col].iloc[idx] = median\n",
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n  self._setitem_with_indexer(indexer, value)\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "d962617d-b45c-4dc5-b3a1-5d7e3549d82a",
        "_uuid": "0697194b36defe623efe785c908927b48c03286f"
      },
      "cell_type": "markdown",
      "source": "## Normalization of contineous data"
    },
    {
      "metadata": {
        "_cell_guid": "988782c0-7bd2-4d5d-9b24-cb8ac45e58a1",
        "_uuid": "71f9b02abd98ad9833fd923538a6cc5e13fe5da5",
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "#df_cont[col] = Normalizer().fit_transform(df_cont[col].reshape(1,-1))[0]",
      "execution_count": 75,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "757b1f49-fa41-4777-83d8-2900968abf2f",
        "_uuid": "3ea34c1a508f92018f178b19cea82eccad1bd770"
      },
      "cell_type": "markdown",
      "source": "# plotting most correlated features(correlation>0.1)"
    },
    {
      "metadata": {
        "_cell_guid": "33c054b4-32cb-42bf-9faa-e32ea62c6cba",
        "_uuid": "576f7a179a181562a00945af47db5bfdc14d9c5b",
        "trusted": true
      },
      "cell_type": "code",
      "source": "\nimport seaborn as sns\ncorrmat = df_cont.corr()\nplt.figure(figsize = (10,7))\n\n\ncorr_features = corrmat.index[abs(corrmat[\"imdb_score\"])>0.1]\ng = sns.heatmap(train[corr_features].corr(),annot=True,cmap=\"RdYlGn\")\n",
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f6f23999668>",
            "image/png": 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AtWvXsnPnTgYNGlTjckuXLmXw4MHExMRQXl6OVqvlypUrnD17lsWLF+PhYXgEPGLECH0S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\n58cbb7zB/v37+eyzz2jZsiXt27fH39+fGTNmVLuvy+d6jhgxghMnTrB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emTNnMmTIEIKC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\ntNj/A9pEAHX1UqqgAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "023a8d29d314495abe0427949250e6f1a7a882f3"
      },
      "cell_type": "markdown",
      "source": "here i tried to plot the most correlated feature which are affectin the target value"
    },
    {
      "metadata": {
        "_cell_guid": "6b075d19-fa5c-4678-8d4f-264e8c941bfb",
        "_uuid": "0b4f6852d70465ab6bd79cd64f0733a45e0c9a88"
      },
      "cell_type": "markdown",
      "source": "# for categorical data"
    },
    {
      "metadata": {
        "_cell_guid": "da349b8b-e29f-4ac8-8026-a6b28e55f915",
        "_uuid": "c54bb26e1c27da3bb29883fd39d4f264a4c01790"
      },
      "cell_type": "markdown",
      "source": "## Categorical Variable Genre\nsplitting it in multiple parts and create column genere_0 to genere_7"
    },
    {
      "metadata": {
        "_cell_guid": "1fd2ed58-1815-4af5-bbed-f303df1b2232",
        "_uuid": "f85be9f34e961f1ce2f09a73d85573ab696a8fc1",
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "genresdf = pd.DataFrame(df_cat['genres'])\ngenresdf = pd.DataFrame(genresdf.genres.str.split('|').tolist(),columns = [\"Genre_\"+str(i) for i in  range(0,8)] )\n\ngenresdf=genresdf.reindex(df_cat.index)\n\n\ndf_cat.drop('genres',inplace = True, axis = 1)\ndf_cat = df_cat.merge(genresdf,left_index = True,right_index = True)",
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py:3697: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n  errors=errors)\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "6dd69961-129d-46f9-8430-4e14c709b986",
        "_uuid": "22d53d3639f0edf627ef6adf742eaa583797371e"
      },
      "cell_type": "markdown",
      "source": "# Handle Missing Data for Categorical Data\n\nIf any column contains entries of missing data, replace those values with the 'anystr'(we can replace here any string)\n## Applying the sklearn.LabelEncoder\n"
    },
    {
      "metadata": {
        "_cell_guid": "dd9fb457-dc38-49ad-a519-e004cacf59cb",
        "_uuid": "3884c6d44b833d46bf1ba9969f42b3011ecf0230",
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "from sklearn.preprocessing import LabelEncoder\nfor col in df_cat.columns.values:\n    if np.sum(df_cat[col].isnull()) > 0:\n        df_cat[col] = df_cat[col].fillna('anystr')\n        \n    df_cat[col] = LabelEncoder().fit_transform(df_cat[col])\n    ",
      "execution_count": 78,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "99609b9f-10ed-4c7b-8cf9-ed238d11627a",
        "_uuid": "68953ee90fe9491ea4e945e9fc43b762c1066d33"
      },
      "cell_type": "markdown",
      "source": "# merging categorical and contineous data(both are normalised)"
    },
    {
      "metadata": {
        "_cell_guid": "c4f59ef6-9f7c-4b12-b377-28fc6a002273",
        "_uuid": "97d6d66ba9ec69cf28b02326eeda605ba289a477",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_new = df_cont.join(df_cat)\ntargetfet = df_new.imdb_score\n\nfeatures = df_new.drop(['imdb_score'], axis = 1)",
      "execution_count": 79,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "24c8ecb5-cb3e-4789-94a5-c5a93ce8c1fd",
        "_uuid": "203bc0b2dfdf94eb589ebced0a736d98e8a6f834"
      },
      "cell_type": "markdown",
      "source": "# Detailed Visualization"
    },
    {
      "metadata": {
        "_cell_guid": "22f415e8-32dc-46ae-9b06-b8a96eecd4b6",
        "_uuid": "8608c7e554031d3fc5230111d82fe4e8bc1d773c",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "vis_dataset = pd.concat([features, targetfet], axis = 1)",
      "execution_count": 80,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "d0fc7f6e-b17c-4fa0-b044-f69796f71285",
        "_uuid": "a6f155431feaf75726303f0e8777c50c34990862"
      },
      "cell_type": "markdown",
      "source": "## No of Movies vs imdb score"
    },
    {
      "metadata": {
        "_cell_guid": "5700a8e6-91e4-4f61-8e6d-988060107630",
        "_uuid": "938ca9a950e703f09ef5717ff34465292608672e",
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(12,8))\nsns.distplot(df_cont.imdb_score.values, bins=50, kde=False)\nplt.xlabel('imdb_score', fontsize=12)\nplt.show()",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f6f15e15898>",
            "image/png": 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6PSudnr2ps7a232OrKqtSW1ubmhL7Vso1kgz5+UtVaj3J72v6Xc8G+xrDcc6Dxddm6fRs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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "8ecd3c5f-cda3-4006-8001-0d50e2142db5",
        "_uuid": "bb5302c4bd77dc5fa7ef347e908068135b86eaa6"
      },
      "cell_type": "markdown",
      "source": "conclusion: maximum movies are rated b/w 6 to 7.5"
    },
    {
      "metadata": {
        "_cell_guid": "ae62647d-d5c1-4924-93ba-c84b48cf35d6",
        "_uuid": "5e0121d4c38d13de6e39ba33ce7e3b1786cdd914"
      },
      "cell_type": "markdown",
      "source": "## Distplot\nThe following plot shows the distribution of imdb score (target) in the dataset.\n\n"
    },
    {
      "metadata": {
        "_cell_guid": "33e56887-3373-4b6c-a81d-a9e9f13a4118",
        "_uuid": "47ada304ee3ba0eedbfda7308f97227b8cb412e0",
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.distplot(targetfet)",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 82,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f6f16033588>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f6f15e151d0>",
            "image/png": 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Px+/3U1ZW1mL7xMREvF4vlZWVTXO5V1ZW0qdPn3O+RkpKCjfeeCNX\nX311u9+vSHuoW0ZCXmpqKjk5Obz66qs0NDSwfft21qxZ02K7P/7xj9TX17N//36WLl1Kfn7+eZ93\n+fLlHDt2DI/H07SUosfj4eqrr2bTpk0UFxfT2NjI8ePH2bt3Lx6PhwkTJvCf//mfnDx5kkOHDvHG\nG2+cN7i/9rWvsWjRIvbv3w8EFtr+6KOPOrE3RALUcpewcN999zFv3jzuvPNOhg4dyjXXXMOJEyea\nbTN8+HDuu+8+HMdh6tSpbS7avH79el5++WXq6+tJTU3lRz/6EdHR0aSkpPDII4/wu9/9jt/85jfE\nx8czY8YMsrOzmTlzJi+++CI/+MEPiI6OpqCggMmTJ5/zNcaNG0ddXR1PP/00lZWVxMfHM2LEiKaD\ntyIdpcU6RETCkLplRETCkLplJKL94he/YNu2bS1unz59OjfeeKMLFYl0DXXLiIiEIXXLiIiEIYW7\niEgYUriLiIQhhbuISBhSuIuIhKH/DyhRGeF6kqMgAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "aac1bfb4-a629-4971-a3d3-694d70b75dba",
        "_uuid": "009692a931b1af1417aa35026076ad5bd36eab94"
      },
      "cell_type": "markdown",
      "source": ""
    },
    {
      "metadata": {
        "_cell_guid": "2ed85547-e29a-4339-8bd4-d5a0f2dfe67e",
        "_uuid": "91ca16daa9fe9ab530833a9dfb4ad9fa30e9d61d"
      },
      "cell_type": "markdown",
      "source": "## budget of movie changing by year\n"
    },
    {
      "metadata": {
        "_cell_guid": "6fee4553-fc2a-459c-a775-61ee5661ceb6",
        "_uuid": "801e3fb1b3b0b2cf163a7aff6547cde397bcee25",
        "trusted": true
      },
      "cell_type": "code",
      "source": "data_groupby_year = train.groupby(train[\"title_year\"])\ndata_groupby_year_mean = data_groupby_year.mean()\nBudget = plt.scatter(data_groupby_year_mean.index, data_groupby_year_mean[\"budget\"],color = \"b\" ,s = data_groupby_year[\"budget\"].count())\nplt.xlabel(\"Year\")\nplt.show()",
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f6f23dd65f8>",
            "image/png": 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hNKq0bevezMS9haCoa1LeEULUmCeeCGfjRhM5Oe5osdt17N9v5NFHI9FkiXuf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\n5pKPR4QAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "7ac3428d-9745-4d11-bf08-9c96135203ce",
        "_uuid": "2d5daf68d177ffdb2b9d118b8d40f806e3d1fa17"
      },
      "cell_type": "markdown",
      "source": "Scatter plot"
    },
    {
      "metadata": {
        "_cell_guid": "5b217b46-7f9e-4805-97d2-82c4851c9c09",
        "_uuid": "4df238b6b3bcdcf8b6dc1c5d224d0a4c64fdb502"
      },
      "cell_type": "markdown",
      "source": "## Boxoffice hit (Gross) of movie changing by year"
    },
    {
      "metadata": {
        "_cell_guid": "43298cd5-d5f2-4728-82ba-dddbbfa45d3b",
        "_uuid": "f41047f966dbb77a66d9890a13fbe1ef4f24be56",
        "trusted": true
      },
      "cell_type": "code",
      "source": "Gross = plt.scatter(data_groupby_year_mean.index, data_groupby_year_mean[\"gross\"],color='r', s = data_groupby_year[\"gross\"].count())\nplt.xlabel(\"Year\")\nplt.show()",
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f6f242245c0>",
            "image/png": 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/n4iIiFrrhNUYfyM+Pp433ngDqDojyKwx6JPVaiUmJqbZQQshWl/JjBkEbt5c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\nTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "36965a16-150c-4553-ad24-7c34e6b35c3c",
        "_uuid": "ecc755f15f32b0abc29556d5fe96367542752946"
      },
      "cell_type": "markdown",
      "source": "## language of movie\n"
    },
    {
      "metadata": {
        "_cell_guid": "ae6d86d7-8987-4460-a566-7204c60ec889",
        "_uuid": "2e50c8ba9d5a79d6ffed4de60f112cf8da37887e"
      },
      "cell_type": "markdown",
      "source": "used BoxPlot"
    },
    {
      "metadata": {
        "_cell_guid": "709bca20-8911-41d5-869e-22d1b994a90d",
        "_uuid": "33de65a66895a86fe675a703290e2c4d5073de5b",
        "trusted": true
      },
      "cell_type": "code",
      "source": "train.boxplot(column=\"gross\", by=\"language\", rot= 90, grid=False)",
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 85,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f6f0f57a828>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f6f15e0add8>",
            "image/png": 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MxOrVq9GrVy9thUb+z6BBg3g9IrTJ2NhYa41t1XvfKHTo0AFeXl7Ys2cPrxuo0HKMMe49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\nERF5Qfl/HbUi/mJxZUYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "8090f259-2b3f-4631-bf1b-1aa21f398acc",
        "_uuid": "c17dd0c78ffce44214cf1788562616aaa62f1bb3"
      },
      "cell_type": "markdown",
      "source": "Gross of english movies are quit high"
    },
    {
      "metadata": {
        "_cell_guid": "649f2b17-7ec1-429a-a137-37cfeb36f69c",
        "_uuid": "9c4ee815abeb92a3c1ebf3d772c556b20a1e347c"
      },
      "cell_type": "markdown",
      "source": "# splitting train and test data"
    },
    {
      "metadata": {
        "_cell_guid": "d5a54deb-9e16-4d9f-a5e0-95437cbe09e6",
        "_uuid": "ffb43b8c3957b07c71fdefb6f4827024c780b7d2",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "X_train, X_test, y_train, y_test = train_test_split(features, targetfet ,test_size=0.3, random_state=7)",
      "execution_count": 86,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "3519eb0f-5169-490d-833e-e78105cb0e69",
        "_uuid": "10cd6efa0f82c7c1068bd702516216e6ed45376f"
      },
      "cell_type": "markdown",
      "source": "# plotting feature importance"
    },
    {
      "metadata": {
        "_cell_guid": "82053278-e776-42e4-a8d5-8d8d3d42b23d",
        "_uuid": "412a0fe3e5fc73e69d1b273da924a5fabaefdd2c",
        "trusted": true
      },
      "cell_type": "code",
      "source": "import xgboost as xgb\nxgb = xgb.XGBRegressor()\nxgb.fit(X_train,y_train)\npredictions_xgb = xgb.predict(X_test)\nerror_xgb = metrics.mean_squared_error(y_test, predictions_xgb)\nprint(error_xgb)",
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": "0.5509213694162202\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "f67d31c8-973b-450e-8fa6-9246fb4ed233",
        "_uuid": "95a2a5c11cb6e99835e847f1cb236cea11d7d3ae",
        "trusted": true
      },
      "cell_type": "code",
      "source": "importances = xgb.feature_importances_\nfeature_names = features.columns.values\ndata = pd.DataFrame({'features': feature_names,'importances':importances})\nnew_index = (data['importances'].sort_values(ascending=False)).index.values\nsorted_data = data.reindex(new_index)\nplt.figure(figsize=(15,10))\nax= sns.barplot(x=sorted_data['features'], y=sorted_data['importances'])\nplt.xticks(rotation= 90)\nplt.xlabel('Features')\nplt.ylabel('Importances')\nplt.title('feature importances')",
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 88,
          "data": {
            "text/plain": "Text(0.5,1,'feature importances')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f6f24226438>",
            "image/png": 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DhkRhtVrd7litbwGtVCpZsWJFvvCFL6S1tTUzZ87MwoULM2HChF7rJk2alEmTJvU8Xr9+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\nxqCQiIiIiIhIwRgUEhERERERKdj/A3uDgnEOMfabAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "f32b8376-344c-446e-8dfc-42a8961c2681",
        "_uuid": "8adef08e26301bb7c281d514099ac78f42290794"
      },
      "cell_type": "markdown",
      "source": "voted users has the hieghest feature importance"
    },
    {
      "metadata": {
        "_cell_guid": "e09c1519-bba7-41f1-8226-426d4683fe7f",
        "_uuid": "320def6d09aedd4fec25b0f0a249e485c7812369"
      },
      "cell_type": "markdown",
      "source": "# linear regression"
    },
    {
      "metadata": {
        "_cell_guid": "09f9120e-cf89-4e93-9bf8-11806b243d34",
        "_uuid": "0728aed3e3ed3f5b9df2fce886eb60f2b2210dab",
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.linear_model import LinearRegression\nclf1 = LinearRegression()\nclf1.fit(X_train, y_train)\npredictions_lr = clf1.predict(X_test)\nerror_lr = metrics.mean_squared_error(y_test, predictions_lr)\nprint(error_lr)",
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": "0.8359186343388991\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "abcfd9257d799e0c192ae1821228ce006b8b33ae"
      },
      "cell_type": "markdown",
      "source": "# Linear Regression fit"
    },
    {
      "metadata": {
        "_uuid": "607a2a17643b8c0b465c229de0898ebd49dfafc4",
        "trusted": true
      },
      "cell_type": "code",
      "source": "y_pred = clf1.predict(X_test)\nplt.figure(figsize=(10, 5))\nplt.scatter(y_test, y_pred, s=20)\nplt.title('Predicted vs. Actual')\nplt.xlabel('Actual Sale Price')\nplt.ylabel('Predicted imdb score')\n\nplt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)])\nplt.tight_layout()",
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f6f096cac50>",
            "image/png": 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lOP3000PVFSIiIqLIwsU8HNUkKRYzZ87EunXrUFJSgvHjx+Paa6/FFVdcgRkzZmDRokVI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\nItKMCrKIiIiISDP/C3sjo8Eku8G/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "e49f0ca2d9e016289c2afacc089adb19557fb16f"
      },
      "cell_type": "markdown",
      "source": "here getting too much noise so trying next algorithm"
    },
    {
      "metadata": {
        "_cell_guid": "b8946aaa-0169-4504-919e-ec34426e429c",
        "_uuid": "8eb12c0df0f0bac943b79acd734a1d03b35d4645"
      },
      "cell_type": "markdown",
      "source": "# Random forest\nRandom Forest, the process es of finding the root node and splitting the feature so that the nodes will run randomly"
    },
    {
      "metadata": {
        "_cell_guid": "8deed635-2b0a-4671-8cbe-dd12352d9588",
        "_uuid": "fe80179eb44c19b34873873e2091e906bd3a0260",
        "trusted": true
      },
      "cell_type": "code",
      "source": "clf = RandomForestRegressor()\n\nclf.fit(X_train, y_train)\npredictions_lr = clf.predict(X_test)\nerror_lr = metrics.mean_squared_error(y_test, predictions_lr)\nprint(error_lr)",
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'RandomForestRegressor' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-91-f12310b8ec9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpredictions_lr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0merror_lr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions_lr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'RandomForestRegressor' is not defined"
          ]
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "5aa78073-a151-4493-a1d8-42d16d3fbd95",
        "_uuid": "9b777e54577086cf31f5c3dcf61d8f4285d0d9bb"
      },
      "cell_type": "markdown",
      "source": "# xgboost\nXGBoost stands for eXtreme Gradient Boosting.\n\nGradient boosting is an approach where new models are created that predict the residuals or errors of prior models and then added together to make the final prediction."
    },
    {
      "metadata": {
        "_cell_guid": "5e313d48-3486-4f11-ad5c-db1902c7b94d",
        "_uuid": "20855e14b6f08c6702ed07c3fc67cd20ae418e99",
        "trusted": true
      },
      "cell_type": "code",
      "source": "import xgboost as xgb\nxgb = xgb.XGBRegressor()\nxgb.fit(X_train,y_train)\npredictions_xgb = xgb.predict(X_test)\nerror_xgb = metrics.mean_squared_error(y_test, predictions_xgb)\nprint(error_xgb)",
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": "0.5509213694162202\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "c6857b02d3916ace1ca3d21e824c3c155075b31e",
        "trusted": true
      },
      "cell_type": "code",
      "source": "y_pred",
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 93,
          "data": {
            "text/plain": "array([7.18919997, 6.41135326, 6.11607596, ..., 6.55898085, 6.24792516,\n       6.40760824])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "92ae95eac5573ee994e00f28f12e8bbee0abf5b0"
      },
      "cell_type": "markdown",
      "source": "# XGBOOST graph fit\n"
    },
    {
      "metadata": {
        "_uuid": "7d0d806832a5034c4b7c0ddfdcd9123c2e789676",
        "trusted": true
      },
      "cell_type": "code",
      "source": "y_pred = xgb.predict(X_test)\n\nplt.figure(figsize=(10, 5))\nplt.scatter(y_test, y_pred, s=20)\nplt.title('Predicted vs. Actual')\nplt.xlabel('Actual Sale Price')\nplt.ylabel('Predicted imdb score')\n\nplt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)])\nplt.tight_layout()\n",
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f6f0953cc18>",
            "image/png": 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NDnM4wH753rna3eYNQIwdSternGkUuW3D3b2IFpIAef/+/ejQoQNiY2MBAGeeeSZ++OEH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\npIb/D/8yGXVbys9dAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "bfd22d81-3998-4bad-8c81-51a61f572c88",
        "_uuid": "3a4baaa777406736f376bf42cd81edac219df649"
      },
      "cell_type": "markdown",
      "source": "# xgboost for finding training and testing score "
    },
    {
      "metadata": {
        "_cell_guid": "e9349460-c774-4769-8c3b-671d3eddb348",
        "_uuid": "f34b56d395fbdd3e9c6b2a6689cf2bf199867e50",
        "trusted": true
      },
      "cell_type": "code",
      "source": "\nimport xgboost as xgb\nxgb = xgb.XGBRegressor()\n# train the model on the training set\nxgb.fit(X_train,y_train)\n\n",
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 95,
          "data": {
            "text/plain": "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n       silent=True, subsample=1)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "bc6fcf5b-4608-4831-9bda-98c4a9c42264",
        "_uuid": "f4d5776813b47b1c080026968ec1631b09bcd3c6",
        "trusted": true
      },
      "cell_type": "code",
      "source": "xgb_score_train = xgb.score(X_test, y_test)\nxgb_score_test = xgb.score(X_train, y_train)\nprint(\"Training score: \",xgb_score_train)\nprint(\"Testing score: \",xgb_score_test)",
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Training score:  0.5556868564775662\nTesting score:  0.6517179856228006\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "bd646573-85a3-40e1-8e98-d47338f99817",
        "_uuid": "eb15008e8a7e23f2fb1a8d9725fc4ce0ab814264"
      },
      "cell_type": "markdown",
      "source": "#  random forest for finding training and testing score "
    },
    {
      "metadata": {
        "_cell_guid": "49d54319-0e71-488c-965d-9fb6b60a8fe5",
        "_uuid": "2ac4b8849e6a0038ef551cbc7ef47ead65611fde",
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.ensemble import RandomForestRegressor\ndt = RandomForestRegressor(n_estimators = 1000,n_jobs=-1,random_state = 0)\ndt.fit(X_train, y_train)\ndt_score_train = dt.score(X_train, y_train)\nprint(\"Training score: \",dt_score_train)\ndt_score_test = dt.score(X_test, y_test)\nprint(\"Testing score: \",dt_score_test)",
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Training score:  0.934768172534712\nTesting score:  0.5333061628749185\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "f8ca962a-2b20-4cfb-8fc2-0bbf6d63f6a4",
        "_uuid": "04e82aaf3807d5d56ef3e3e9e4cc29a2152bf876",
        "collapsed": true
      },
      "cell_type": "markdown",
      "source": "# so here most sutable algorithm is XGBoost"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "85821e947ee4440e77da845aaae806d35e3d7eea"
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "1bf79d10dfc55ac9b605a377fcf60cc8b73304e8"
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.6",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 1
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