Employee Atrition Rate with score 81.523.txt

{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Ridge Implementation "},{"metadata":{},"cell_type":"markdown","source":"## Importing Necessary Libraries"},{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn import metrics\n\nfrom sklearn.linear_model import LinearRegression \nfrom sklearn.linear_model import Ridge\nfrom sklearn.linear_model import Lasso\n\nfrom sklearn.linear_model import Lasso\nfrom sklearn.linear_model import ElasticNet\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.tree import DecisionTreeRegressor\n\nfrom sklearn.metrics import mean_squared_error","execution_count":1,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Let's find out the path of input files."},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","execution_count":2,"outputs":[{"output_type":"stream","text":"/kaggle/input/sample_submission.csv\n/kaggle/input/Test.csv\n/kaggle/input/Train.csv\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Reading Dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.read_csv(\"/kaggle/input/Train.csv\") \ntest = pd.read_csv(\"/kaggle/input/Test.csv\")","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.columns","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"Index(['Employee_ID', 'Gender', 'Age', 'Education_Level',\n       'Relationship_Status', 'Hometown', 'Unit', 'Decision_skill_possess',\n       'Time_of_service', 'Time_since_promotion', 'growth_rate', 'Travel_Rate',\n       'Post_Level', 'Pay_Scale', 'Compensation_and_Benefits',\n       'Work_Life_balance', 'VAR1', 'VAR2', 'VAR3', 'VAR4', 'VAR5', 'VAR6',\n       'VAR7', 'Attrition_rate'],\n      dtype='object')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(train.shape)\ntrain.head()","execution_count":5,"outputs":[{"output_type":"stream","text":"(7000, 24)\n","name":"stdout"},{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"  Employee_ID Gender   Age  Education_Level Relationship_Status     Hometown  \\\n0   EID_23371      F  42.0                4             Married     Franklin   \n1   EID_18000      M  24.0                3              Single  Springfield   \n2    EID_3891      F  58.0                3             Married      Clinton   \n3   EID_17492      F  26.0                3              Single      Lebanon   \n4   EID_22534      F  31.0                1             Married  Springfield   \n\n                        Unit Decision_skill_possess  Time_of_service  \\\n0                         IT             Conceptual              4.0   \n1                  Logistics             Analytical              5.0   \n2                    Quality             Conceptual             27.0   \n3  Human Resource Management             Behavioral              4.0   \n4                  Logistics             Conceptual              5.0   \n\n   Time_since_promotion  ...  Compensation_and_Benefits  Work_Life_balance  \\\n0                     4  ...                      type2                3.0   \n1                     4  ...                      type2                4.0   \n2                     3  ...                      type2                1.0   \n3                     3  ...                      type2                1.0   \n4                     4  ...                      type3                3.0   \n\n   VAR1    VAR2    VAR3  VAR4  VAR5  VAR6  VAR7  Attrition_rate  \n0     4  0.7516  1.8688   2.0     4     5     3          0.1841  \n1     3 -0.9612 -0.4537   2.0     3     5     3          0.0670  \n2     4 -0.9612 -0.4537   3.0     3     8     3          0.0851  \n3     3 -1.8176 -0.4537   NaN     3     7     3          0.0668  \n4     1  0.7516 -0.4537   2.0     2     8     2          0.1827  \n\n[5 rows x 24 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>Employee_ID</th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>Education_Level</th>\n      <th>Relationship_Status</th>\n      <th>Hometown</th>\n      <th>Unit</th>\n      <th>Decision_skill_possess</th>\n      <th>Time_of_service</th>\n      <th>Time_since_promotion</th>\n      <th>...</th>\n      <th>Compensation_and_Benefits</th>\n      <th>Work_Life_balance</th>\n      <th>VAR1</th>\n      <th>VAR2</th>\n      <th>VAR3</th>\n      <th>VAR4</th>\n      <th>VAR5</th>\n      <th>VAR6</th>\n      <th>VAR7</th>\n      <th>Attrition_rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>EID_23371</td>\n      <td>F</td>\n      <td>42.0</td>\n      <td>4</td>\n      <td>Married</td>\n      <td>Franklin</td>\n      <td>IT</td>\n      <td>Conceptual</td>\n      <td>4.0</td>\n      <td>4</td>\n      <td>...</td>\n      <td>type2</td>\n      <td>3.0</td>\n      <td>4</td>\n      <td>0.7516</td>\n      <td>1.8688</td>\n      <td>2.0</td>\n      <td>4</td>\n      <td>5</td>\n      <td>3</td>\n      <td>0.1841</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>EID_18000</td>\n      <td>M</td>\n      <td>24.0</td>\n      <td>3</td>\n      <td>Single</td>\n      <td>Springfield</td>\n      <td>Logistics</td>\n      <td>Analytical</td>\n      <td>5.0</td>\n      <td>4</td>\n      <td>...</td>\n      <td>type2</td>\n      <td>4.0</td>\n      <td>3</td>\n      <td>-0.9612</td>\n      <td>-0.4537</td>\n      <td>2.0</td>\n      <td>3</td>\n      <td>5</td>\n      <td>3</td>\n      <td>0.0670</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>EID_3891</td>\n      <td>F</td>\n      <td>58.0</td>\n      <td>3</td>\n      <td>Married</td>\n      <td>Clinton</td>\n      <td>Quality</td>\n      <td>Conceptual</td>\n      <td>27.0</td>\n      <td>3</td>\n      <td>...</td>\n      <td>type2</td>\n      <td>1.0</td>\n      <td>4</td>\n      <td>-0.9612</td>\n      <td>-0.4537</td>\n      <td>3.0</td>\n      <td>3</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0.0851</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>EID_17492</td>\n      <td>F</td>\n      <td>26.0</td>\n      <td>3</td>\n      <td>Single</td>\n      <td>Lebanon</td>\n      <td>Human Resource Management</td>\n      <td>Behavioral</td>\n      <td>4.0</td>\n      <td>3</td>\n      <td>...</td>\n      <td>type2</td>\n      <td>1.0</td>\n      <td>3</td>\n      <td>-1.8176</td>\n      <td>-0.4537</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>7</td>\n      <td>3</td>\n      <td>0.0668</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>EID_22534</td>\n      <td>F</td>\n      <td>31.0</td>\n      <td>1</td>\n      <td>Married</td>\n      <td>Springfield</td>\n      <td>Logistics</td>\n      <td>Conceptual</td>\n      <td>5.0</td>\n      <td>4</td>\n      <td>...</td>\n      <td>type3</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>0.7516</td>\n      <td>-0.4537</td>\n      <td>2.0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.1827</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 24 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.describe()","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"               Age  Education_Level  Time_of_service  Time_since_promotion  \\\ncount  6588.000000      7000.000000      6856.000000           7000.000000   \nmean     39.622799         3.187857        13.385064              2.367143   \nstd      13.606920         1.065102        10.364188              1.149395   \nmin      19.000000         1.000000         0.000000              0.000000   \n25%      27.000000         3.000000         5.000000              1.000000   \n50%      37.000000         3.000000        10.000000              2.000000   \n75%      52.000000         4.000000        21.000000              3.000000   \nmax      65.000000         5.000000        43.000000              4.000000   \n\n       growth_rate  Travel_Rate   Post_Level    Pay_Scale  Work_Life_balance  \\\ncount  7000.000000  7000.000000  7000.000000  6991.000000        6989.000000   \nmean     47.064286     0.817857     2.798000     6.006294           2.387895   \nstd      15.761406     0.648205     1.163721     2.058435           1.122786   \nmin      20.000000     0.000000     1.000000     1.000000           1.000000   \n25%      33.000000     0.000000     2.000000     5.000000           1.000000   \n50%      47.000000     1.000000     3.000000     6.000000           2.000000   \n75%      61.000000     1.000000     3.000000     8.000000           3.000000   \nmax      74.000000     2.000000     5.000000    10.000000           5.000000   \n\n              VAR1         VAR2         VAR3         VAR4         VAR5  \\\ncount  7000.000000  6423.000000  7000.000000  6344.000000  7000.000000   \nmean      3.098571    -0.008126    -0.013606     1.891078     2.834143   \nstd       0.836377     0.989850     0.986933     0.529403     0.938945   \nmin       1.000000    -1.817600    -2.776200     1.000000     1.000000   \n25%       3.000000    -0.961200    -0.453700     2.000000     2.000000   \n50%       3.000000    -0.104800    -0.453700     2.000000     3.000000   \n75%       3.000000     0.751600     0.707500     2.000000     3.000000   \nmax       5.000000     1.608100     1.868800     3.000000     5.000000   \n\n              VAR6         VAR7  Attrition_rate  \ncount  7000.000000  7000.000000     7000.000000  \nmean      7.101286     3.257000        0.189376  \nstd       1.164262     0.925319        0.185753  \nmin       5.000000     1.000000        0.000000  \n25%       6.000000     3.000000        0.070400  \n50%       7.000000     3.000000        0.142650  \n75%       8.000000     4.000000        0.235000  \nmax       9.000000     5.000000        0.995900  ","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>Age</th>\n      <th>Education_Level</th>\n      <th>Time_of_service</th>\n      <th>Time_since_promotion</th>\n      <th>growth_rate</th>\n      <th>Travel_Rate</th>\n      <th>Post_Level</th>\n      <th>Pay_Scale</th>\n      <th>Work_Life_balance</th>\n      <th>VAR1</th>\n      <th>VAR2</th>\n      <th>VAR3</th>\n      <th>VAR4</th>\n      <th>VAR5</th>\n      <th>VAR6</th>\n      <th>VAR7</th>\n      <th>Attrition_rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>6588.000000</td>\n      <td>7000.000000</td>\n      <td>6856.000000</td>\n      <td>7000.000000</td>\n      <td>7000.000000</td>\n      <td>7000.000000</td>\n      <td>7000.000000</td>\n      <td>6991.000000</td>\n      <td>6989.000000</td>\n      <td>7000.000000</td>\n      <td>6423.000000</td>\n      <td>7000.000000</td>\n      <td>6344.000000</td>\n      <td>7000.000000</td>\n      <td>7000.000000</td>\n      <td>7000.000000</td>\n      <td>7000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>39.622799</td>\n      <td>3.187857</td>\n      <td>13.385064</td>\n      <td>2.367143</td>\n      <td>47.064286</td>\n      <td>0.817857</td>\n      <td>2.798000</td>\n      <td>6.006294</td>\n      <td>2.387895</td>\n      <td>3.098571</td>\n      <td>-0.008126</td>\n      <td>-0.013606</td>\n      <td>1.891078</td>\n      <td>2.834143</td>\n      <td>7.101286</td>\n      <td>3.257000</td>\n      <td>0.189376</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>13.606920</td>\n      <td>1.065102</td>\n      <td>10.364188</td>\n      <td>1.149395</td>\n      <td>15.761406</td>\n      <td>0.648205</td>\n      <td>1.163721</td>\n      <td>2.058435</td>\n      <td>1.122786</td>\n      <td>0.836377</td>\n      <td>0.989850</td>\n      <td>0.986933</td>\n      <td>0.529403</td>\n      <td>0.938945</td>\n      <td>1.164262</td>\n      <td>0.925319</td>\n      <td>0.185753</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>19.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>20.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>-1.817600</td>\n      <td>-2.776200</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>5.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>27.000000</td>\n      <td>3.000000</td>\n      <td>5.000000</td>\n      <td>1.000000</td>\n      <td>33.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>5.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>-0.961200</td>\n      <td>-0.453700</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>6.000000</td>\n      <td>3.000000</td>\n      <td>0.070400</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>37.000000</td>\n      <td>3.000000</td>\n      <td>10.000000</td>\n      <td>2.000000</td>\n      <td>47.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>6.000000</td>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>-0.104800</td>\n      <td>-0.453700</td>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>7.000000</td>\n      <td>3.000000</td>\n      <td>0.142650</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>52.000000</td>\n      <td>4.000000</td>\n      <td>21.000000</td>\n      <td>3.000000</td>\n      <td>61.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>8.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>0.751600</td>\n      <td>0.707500</td>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>8.000000</td>\n      <td>4.000000</td>\n      <td>0.235000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>65.000000</td>\n      <td>5.000000</td>\n      <td>43.000000</td>\n      <td>4.000000</td>\n      <td>74.000000</td>\n      <td>2.000000</td>\n      <td>5.000000</td>\n      <td>10.000000</td>\n      <td>5.000000</td>\n      <td>5.000000</td>\n      <td>1.608100</td>\n      <td>1.868800</td>\n      <td>3.000000</td>\n      <td>5.000000</td>\n      <td>9.000000</td>\n      <td>5.000000</td>\n      <td>0.995900</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.info()","execution_count":7,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 7000 entries, 0 to 6999\nData columns (total 24 columns):\n #   Column                     Non-Null Count  Dtype  \n---  ------                     --------------  -----  \n 0   Employee_ID                7000 non-null   object \n 1   Gender                     7000 non-null   object \n 2   Age                        6588 non-null   float64\n 3   Education_Level            7000 non-null   int64  \n 4   Relationship_Status        7000 non-null   object \n 5   Hometown                   7000 non-null   object \n 6   Unit                       7000 non-null   object \n 7   Decision_skill_possess     7000 non-null   object \n 8   Time_of_service            6856 non-null   float64\n 9   Time_since_promotion       7000 non-null   int64  \n 10  growth_rate                7000 non-null   int64  \n 11  Travel_Rate                7000 non-null   int64  \n 12  Post_Level                 7000 non-null   int64  \n 13  Pay_Scale                  6991 non-null   float64\n 14  Compensation_and_Benefits  7000 non-null   object \n 15  Work_Life_balance          6989 non-null   float64\n 16  VAR1                       7000 non-null   int64  \n 17  VAR2                       6423 non-null   float64\n 18  VAR3                       7000 non-null   float64\n 19  VAR4                       6344 non-null   float64\n 20  VAR5                       7000 non-null   int64  \n 21  VAR6                       7000 non-null   int64  \n 22  VAR7                       7000 non-null   int64  \n 23  Attrition_rate             7000 non-null   float64\ndtypes: float64(8), int64(9), object(7)\nmemory usage: 1.3+ MB\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Checking if there are some missing values in the data or not"},{"metadata":{"trusted":true},"cell_type":"code","source":"train.isna().any()","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"Employee_ID                  False\nGender                       False\nAge                           True\nEducation_Level              False\nRelationship_Status          False\nHometown                     False\nUnit                         False\nDecision_skill_possess       False\nTime_of_service               True\nTime_since_promotion         False\ngrowth_rate                  False\nTravel_Rate                  False\nPost_Level                   False\nPay_Scale                     True\nCompensation_and_Benefits    False\nWork_Life_balance             True\nVAR1                         False\nVAR2                          True\nVAR3                         False\nVAR4                          True\nVAR5                         False\nVAR6                         False\nVAR7                         False\nAttrition_rate               False\ndtype: bool"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Data Visualization and Preprocessing"},{"metadata":{},"cell_type":"markdown","source":"#### Plotting the correlation matrix for the dataset"},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"#Using Pearson Correlation\nplt.figure(figsize=(18,10))\ncor = train.corr()\nsns.heatmap(cor, annot=True, cmap=plt.cm.Accent)\nplt.show()\nplt.savefig(\"main_correlation.png\")","execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1296x720 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 0 Axes>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Labels and Features"},{"metadata":{"trusted":true},"cell_type":"code","source":"label = [\"Attrition_rate\"]\nfeatures = ['VAR7','VAR6','VAR5','VAR1','VAR3','growth_rate','Time_of_service','Time_since_promotion','Travel_Rate','Post_Level','Education_Level']","execution_count":10,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"featured_data = train.loc[:,features+label]\nfeatured_data = featured_data.dropna(axis=0)\nfeatured_data.shape","execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"text/plain":"(6856, 12)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = featured_data.loc[:,features]\ny = featured_data.loc[:,label]","execution_count":12,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Splitting the Dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=1,test_size=0.55)","execution_count":13,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Learning Model"},{"metadata":{"tags":[],"trusted":true},"cell_type":"code","source":"df = Ridge(alpha=0.000001)\ndf.fit(X_train,y_train)\ny_pred = df.predict(X_test)\nc=[]\nfor i in range(len(y_pred)):\n    c.append((y_pred[i][0].round(5)))\npf=c[:3000]\n\nprint(len(c),len(pf),c[0])","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"score = 100* max(0, 1-mean_squared_error(y_test, y_pred))\nprint(score)","execution_count":15,"outputs":[{"output_type":"stream","text":"96.72970811622436\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Data Preprocessing Test file"},{"metadata":{"trusted":true},"cell_type":"code","source":"selected_test = test.loc[:,features]\n#selected_test.info()\nmean_values = np.mean(selected_test)\nselected_test[features].replace(mean_values,np.nan,inplace=True)\nfor i,val in enumerate(features):\n    selected_test[val] = selected_test[val].fillna(mean_values[i])\n    \nselected_test.head()","execution_count":16,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"   VAR7  VAR6  VAR5  VAR1    VAR3  growth_rate  Time_of_service  \\\n0     4     8     1     3 -0.4537           30              7.0   \n1     2     8     2     4  0.7075           72             41.0   \n2     3     9     1     4  0.7075           25             21.0   \n3     3     8     2     3  0.7075           28             11.0   \n4     4     7     2     4  0.7075           47             12.0   \n\n   Time_since_promotion  Travel_Rate  Post_Level  Education_Level  \n0                     4            1           5                5  \n1                     2            1           1                2  \n2                     3            0           1                3  \n3                     4            1           1                5  \n4                     4            1           3                3  ","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>VAR7</th>\n      <th>VAR6</th>\n      <th>VAR5</th>\n      <th>VAR1</th>\n      <th>VAR3</th>\n      <th>growth_rate</th>\n      <th>Time_of_service</th>\n      <th>Time_since_promotion</th>\n      <th>Travel_Rate</th>\n      <th>Post_Level</th>\n      <th>Education_Level</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4</td>\n      <td>8</td>\n      <td>1</td>\n      <td>3</td>\n      <td>-0.4537</td>\n      <td>30</td>\n      <td>7.0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>5</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>8</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.7075</td>\n      <td>72</td>\n      <td>41.0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>9</td>\n      <td>1</td>\n      <td>4</td>\n      <td>0.7075</td>\n      <td>25</td>\n      <td>21.0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>8</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0.7075</td>\n      <td>28</td>\n      <td>11.0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>7</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.7075</td>\n      <td>47</td>\n      <td>12.0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Prediction"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Predicting\nimport pandas as pd\ndff = pd.DataFrame({'Employee_ID':test['Employee_ID'],'Attrition_rate':pf})\n#Converting to CSV\ndff.to_csv(\"Predictions.csv\",index=False)","execution_count":17,"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'pf' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-17-932c9753129e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Predicting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'Employee_ID'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Employee_ID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Attrition_rate'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mpf\u001b[0m\u001b[0;34m}\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      4\u001b[0m \u001b[0;31m#Converting to CSV\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Predictions.csv\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'pf' is not defined"]}]},{"metadata":{},"cell_type":"markdown","source":"#### The final test submission score."},{"metadata":{},"cell_type":"markdown","source":"![final test submission score](https://raw.githubusercontent.com/blurred-machine/HackerEarth-Machine-Learning-Challenge/master/final_test_score.PNG)"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}