Logitic Regression/Logistic Regression.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset is downloaded from Kaggle. Link: https://www.kaggle.com/giripujar/hr-analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n0                0.38             0.53               2                   157   \n1                0.80             0.86               5                   262   \n2                0.11             0.88               7                   272   \n3                0.72             0.87               5                   223   \n4                0.37             0.52               2                   159   \n\n   time_spend_company  Work_accident  left  promotion_last_5years Department  \\\n0                   3              0     1                      0      sales   \n1                   6              0     1                      0      sales   \n2                   4              0     1                      0      sales   \n3                   5              0     1                      0      sales   \n4                   3              0     1                      0      sales   \n\n   salary  \n0     low  \n1  medium  \n2  medium  \n3     low  \n4     low  ",
      "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>satisfaction_level</th>\n      <th>last_evaluation</th>\n      <th>number_project</th>\n      <th>average_montly_hours</th>\n      <th>time_spend_company</th>\n      <th>Work_accident</th>\n      <th>left</th>\n      <th>promotion_last_5years</th>\n      <th>Department</th>\n      <th>salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.38</td>\n      <td>0.53</td>\n      <td>2</td>\n      <td>157</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>sales</td>\n      <td>low</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.80</td>\n      <td>0.86</td>\n      <td>5</td>\n      <td>262</td>\n      <td>6</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>sales</td>\n      <td>medium</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.11</td>\n      <td>0.88</td>\n      <td>7</td>\n      <td>272</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>sales</td>\n      <td>medium</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.72</td>\n      <td>0.87</td>\n      <td>5</td>\n      <td>223</td>\n      <td>5</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>sales</td>\n      <td>low</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.37</td>\n      <td>0.52</td>\n      <td>2</td>\n      <td>159</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>sales</td>\n      <td>low</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "df = pd.read_csv(\"HR_comma_sep.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Exploratory Data Analysis and visualization</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(3571, 10)"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "left = df[df.left==1]\n",
    "left.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(11428, 10)"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "retained = df[df.left==0]\n",
    "retained.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      satisfaction_level  last_evaluation  number_project  \\\nleft                                                        \n0               0.666810         0.715473        3.786664   \n1               0.440098         0.718113        3.855503   \n\n      average_montly_hours  time_spend_company  Work_accident  \\\nleft                                                            \n0               199.060203            3.380032       0.175009   \n1               207.419210            3.876505       0.047326   \n\n      promotion_last_5years  \nleft                         \n0                  0.026251  \n1                  0.005321  ",
      "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>satisfaction_level</th>\n      <th>last_evaluation</th>\n      <th>number_project</th>\n      <th>average_montly_hours</th>\n      <th>time_spend_company</th>\n      <th>Work_accident</th>\n      <th>promotion_last_5years</th>\n    </tr>\n    <tr>\n      <th>left</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.666810</td>\n      <td>0.715473</td>\n      <td>3.786664</td>\n      <td>199.060203</td>\n      <td>3.380032</td>\n      <td>0.175009</td>\n      <td>0.026251</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.440098</td>\n      <td>0.718113</td>\n      <td>3.855503</td>\n      <td>207.419210</td>\n      <td>3.876505</td>\n      <td>0.047326</td>\n      <td>0.005321</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "df.groupby('left').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above table we can draw following conclusions,\n",
    "<ol>\n",
    "    <li>**Satisfaction Level**: Satisfaction level seems to be relatively low (0.44) in employees leaving the firm vs the retained ones (0.66)</li>\n",
    "    <li>**Average Monthly Hours**: Average monthly hours are higher in employees leaving the firm (199 vs 207)</li>\n",
    "    <li>**Promotion Last 5 Years**: Employees who are given promotion are likely to be retained at firm </li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Salary on employee's retention**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x2282403d820>"
     },
     "metadata": {},
     "execution_count": 7
    },
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "pd.crosstab(df.salary,df.left).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above bar chart shows employees with high salaries are likely to not leave the company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Department on employee retention rate**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x2282616bdf0>"
     },
     "metadata": {},
     "execution_count": 8
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "pd.crosstab(df.Department,df.left).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above chart there seem to be some impact of department on employee retention but it is not major hence we will ignore department in our analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>From the above we can infer that the following variables can be taken as independant variables in our model</h3>\n",
    "<ol>\n",
    "    <li>Salary</li>\n",
    "    <li>Satisfaction Level</li>\n",
    "    <li>Average Monthly Hours</li>\n",
    "    <li>Promotion Last 5 Years</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   satisfaction_level  average_montly_hours  promotion_last_5years  salary\n0                0.38                   157                      0     low\n1                0.80                   262                      0  medium\n2                0.11                   272                      0  medium\n3                0.72                   223                      0     low\n4                0.37                   159                      0     low",
      "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>satisfaction_level</th>\n      <th>average_montly_hours</th>\n      <th>promotion_last_5years</th>\n      <th>salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.38</td>\n      <td>157</td>\n      <td>0</td>\n      <td>low</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.80</td>\n      <td>262</td>\n      <td>0</td>\n      <td>medium</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.11</td>\n      <td>272</td>\n      <td>0</td>\n      <td>medium</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.72</td>\n      <td>223</td>\n      <td>0</td>\n      <td>low</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.37</td>\n      <td>159</td>\n      <td>0</td>\n      <td>low</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "subdf = df[['satisfaction_level','average_montly_hours','promotion_last_5years','salary']]\n",
    "subdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**dummy variable in salary**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Salary has all text data. It needs to be converted to numbers and here using dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_dummies = pd.get_dummies(subdf.salary, prefix=\"salary\")\n",
    "df_with_dummies = pd.concat([subdf,salary_dummies],axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   satisfaction_level  average_montly_hours  promotion_last_5years  salary  \\\n0                0.38                   157                      0     low   \n1                0.80                   262                      0  medium   \n2                0.11                   272                      0  medium   \n3                0.72                   223                      0     low   \n4                0.37                   159                      0     low   \n\n   salary_high  salary_low  salary_medium  \n0            0           1              0  \n1            0           0              1  \n2            0           0              1  \n3            0           1              0  \n4            0           1              0  ",
      "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>satisfaction_level</th>\n      <th>average_montly_hours</th>\n      <th>promotion_last_5years</th>\n      <th>salary</th>\n      <th>salary_high</th>\n      <th>salary_low</th>\n      <th>salary_medium</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.38</td>\n      <td>157</td>\n      <td>0</td>\n      <td>low</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.80</td>\n      <td>262</td>\n      <td>0</td>\n      <td>medium</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.11</td>\n      <td>272</td>\n      <td>0</td>\n      <td>medium</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.72</td>\n      <td>223</td>\n      <td>0</td>\n      <td>low</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.37</td>\n      <td>159</td>\n      <td>0</td>\n      <td>low</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "df_with_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   satisfaction_level  average_montly_hours  promotion_last_5years  \\\n0                0.38                   157                      0   \n1                0.80                   262                      0   \n2                0.11                   272                      0   \n3                0.72                   223                      0   \n4                0.37                   159                      0   \n\n   salary_high  salary_low  salary_medium  \n0            0           1              0  \n1            0           0              1  \n2            0           0              1  \n3            0           1              0  \n4            0           1              0  ",
      "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>satisfaction_level</th>\n      <th>average_montly_hours</th>\n      <th>promotion_last_5years</th>\n      <th>salary_high</th>\n      <th>salary_low</th>\n      <th>salary_medium</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.38</td>\n      <td>157</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.80</td>\n      <td>262</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.11</td>\n      <td>272</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.72</td>\n      <td>223</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.37</td>\n      <td>159</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "df_with_dummies.drop('salary',axis='columns',inplace=True)\n",
    "df_with_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   satisfaction_level  average_montly_hours  promotion_last_5years  \\\n0                0.38                   157                      0   \n1                0.80                   262                      0   \n2                0.11                   272                      0   \n3                0.72                   223                      0   \n4                0.37                   159                      0   \n\n   salary_high  salary_low  salary_medium  \n0            0           1              0  \n1            0           0              1  \n2            0           0              1  \n3            0           1              0  \n4            0           1              0  ",
      "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>satisfaction_level</th>\n      <th>average_montly_hours</th>\n      <th>promotion_last_5years</th>\n      <th>salary_high</th>\n      <th>salary_low</th>\n      <th>salary_medium</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.38</td>\n      <td>157</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.80</td>\n      <td>262</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.11</td>\n      <td>272</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.72</td>\n      <td>223</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.37</td>\n      <td>159</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "X = df_with_dummies\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n                   multi_class='auto', n_jobs=None, penalty='l2',\n                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n                   warm_start=False)"
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "7269     0\n8160     0\n6100     0\n2303     0\n10545    0\n        ..\n8327     0\n1072     1\n13937    0\n9426     0\n11412    0\nName: left, Length: 10500, dtype: int64"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[0 0 0 ... 0 0 0]\n"
    }
   ],
   "source": [
    "print(model.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Model Accuracy : 0.7702857142857142\n"
    }
   ],
   "source": [
    "print (\"Model Accuracy : {}\".format(model.score(X_test,y_test)))"
   ]
  }
 ],
 "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": "3.8.2-final"
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 },
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