adult_salary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48842 entries, 0 to 48841\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   age             48842 non-null  int64 \n",
      " 1   workclass       48842 non-null  object\n",
      " 2   fnlwgt          48842 non-null  int64 \n",
      " 3   education       48842 non-null  object\n",
      " 4   education-num   48842 non-null  int64 \n",
      " 5   marital-status  48842 non-null  object\n",
      " 6   occupation      48842 non-null  object\n",
      " 7   relationship    48842 non-null  object\n",
      " 8   race            48842 non-null  object\n",
      " 9   gender          48842 non-null  object\n",
      " 10  capital-gain    48842 non-null  int64 \n",
      " 11  capital-loss    48842 non-null  int64 \n",
      " 12  hours-per-week  48842 non-null  int64 \n",
      " 13  native-country  48842 non-null  object\n",
      " 14  salary          48842 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 5.6+ MB\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=pd.read_csv(\"adult11.csv\")\n",
    "data.head()\n",
    "data.info()\n",
    "data.describe()\n",
    "data.hist(bins=50, figsize=(20,15))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age               0\n",
      "workclass         0\n",
      "fnlwgt            0\n",
      "education         0\n",
      "education-num     0\n",
      "marital-status    0\n",
      "occupation        0\n",
      "relationship      0\n",
      "race              0\n",
      "gender            0\n",
      "capital-gain      0\n",
      "capital-loss      0\n",
      "hours-per-week    0\n",
      "native-country    0\n",
      "salary            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data.dropna(inplace=True)\n",
    "print(data.isnull().sum())\n",
    "data=pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.head()\n",
    "data=data.drop(\"salary_<=50K\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 0.8186098884225612\n",
      "Accuracy 0.8186098884225612\n",
      "Confusion Metrics\n",
      " [[6521  958]\n",
      " [ 814 1476]]\n",
      "Classification Report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       False       0.89      0.87      0.88      7479\n",
      "        True       0.61      0.64      0.62      2290\n",
      "\n",
      "    accuracy                           0.82      9769\n",
      "   macro avg       0.75      0.76      0.75      9769\n",
      "weighted avg       0.82      0.82      0.82      9769\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "\n",
    "X=data.drop(\"salary_>50K\",axis=1)\n",
    "y=data[\"salary_>50K\"]\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)\n",
    "\n",
    "scaler=StandardScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)\n",
    "\n",
    "dt_model=DecisionTreeClassifier()\n",
    "dt_model.fit(X_train,y_train)\n",
    "\n",
    "y_pred=dt_model.predict(X_test)\n",
    "print(\"Model\",dt_model.score(X_test,y_test))\n",
    "print(\"Accuracy\",accuracy_score(y_test,y_pred))\n",
    "print(\"Confusion Metrics\\n\",confusion_matrix(y_test,y_pred))\n",
    "print(\"Classification Report\\n\",classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8596874360199277\n",
      "Confusion Matrix:\n",
      " [[10413   820]\n",
      " [ 1236  2184]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       False       0.89      0.93      0.91     11233\n",
      "        True       0.73      0.64      0.68      3420\n",
      "\n",
      "    accuracy                           0.86     14653\n",
      "   macro avg       0.81      0.78      0.80     14653\n",
      "weighted avg       0.85      0.86      0.86     14653\n",
      "\n",
      "Feature Importances:\n",
      " [1.50886698e-01 1.66085901e-01 6.43616267e-02 9.05435824e-02\n",
      " 2.98546325e-02 8.41103196e-02 1.81543660e-03 5.03724418e-03\n",
      " 5.53223898e-03 3.52198666e-06 1.01766916e-02 6.52551302e-03\n",
      " 7.88526044e-03 4.53960162e-03 1.33789342e-04 1.57839905e-03\n",
      " 1.87509092e-03 6.56130913e-04 2.27467489e-04 8.28605501e-04\n",
      " 1.85121434e-03 1.09408323e-03 2.45564792e-03 2.90778408e-03\n",
      " 1.21539677e-02 3.15676821e-03 6.48551419e-03 9.23196171e-03\n",
      " 5.74574022e-05 3.80165635e-03 5.35184982e-03 6.22316853e-03\n",
      " 2.93485889e-04 5.17410915e-02 9.72847306e-04 2.06941695e-02\n",
      " 1.54086900e-03 1.49403312e-03 1.82223786e-03 5.41524469e-03\n",
      " 8.65742086e-05 6.32943359e-03 1.60379187e-02 4.16189189e-03\n",
      " 3.08995460e-03 3.91193374e-03 6.67328183e-03 1.41968797e-04\n",
      " 1.62478881e-02 2.88656317e-03 6.87203517e-03 4.24812908e-03\n",
      " 4.31486749e-03 5.81610167e-02 1.05460150e-02 1.59525527e-03\n",
      " 9.62874049e-03 5.37633162e-03 1.12650490e-02 1.25705892e-03\n",
      " 3.14817490e-03 4.68847171e-03 8.38021760e-04 6.42575648e-03\n",
      " 7.88174452e-03 8.46557128e-03 2.52391265e-03 2.67868932e-04\n",
      " 1.16439541e-03 5.90014009e-04 2.24611996e-04 6.68735115e-04\n",
      " 2.05021136e-04 1.30845300e-04 2.09210490e-04 6.55969202e-04\n",
      " 3.75196848e-04 7.61935707e-04 4.38321500e-04 1.06461552e-04\n",
      " 1.90362537e-04 2.30809349e-08 2.36049126e-05 1.18010196e-04\n",
      " 1.31719413e-04 7.07736763e-04 5.54278505e-04 2.60286873e-04\n",
      " 5.41341802e-04 4.48457525e-04 4.74966524e-04 8.75710956e-05\n",
      " 2.05854441e-03 1.64871173e-04 3.87408775e-05 2.43691702e-04\n",
      " 9.32509904e-04 5.21007215e-04 3.08951446e-04 4.38857936e-04\n",
      " 9.14059840e-05 4.92948436e-04 2.33947943e-04 7.73788520e-05\n",
      " 9.37484741e-05 6.15979286e-03 3.00428152e-04 2.99864483e-04]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "X = data.drop(\"salary_>50K\", axis=1)\n",
    "y = data[\"salary_>50K\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "rf_model = RandomForestClassifier()\n",
    "rf_model.fit(X_train, y_train)\n",
    "y_pred = rf_model.predict(X_test)\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))\n",
    "print(\"Feature Importances:\\n\", rf_model.feature_importances_)"
   ]
  }
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