21f1000788-mlp-project.ipynb

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   "source": [
    "# Marketing Strategy - Personalised Offer\n",
    "# -"
   ]
  },
  {
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    "tags": []
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
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   "outputs": [],
   "source": [
    "train = pd.read_csv('/kaggle/input/marketing-strategy-personalised-offer/train_data.csv')\n",
    "test = pd.read_csv('/kaggle/input/marketing-strategy-personalised-offer/test_data.csv')"
   ]
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     "text": [
      "(12379, 31) (5305, 30)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape,test.shape)"
   ]
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      "(12379, 30) (12379,)\n",
      "(12379, 31)\n"
     ]
    }
   ],
   "source": [
    "train_x = train.drop('Offer Accepted',axis='columns')\n",
    "train_y = train['Offer Accepted']\n",
    "print(train_x.shape,train_y.shape)\n",
    "print(train.shape)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "id": "a1800e37",
   "metadata": {
    "execution": {
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      "(12379, 28) (5305, 28)\n"
     ]
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   ],
   "source": [
    "# cols = ['car','travelled_more_than_5mins_for_offer','Job/Job Industry']\n",
    "cols = ['car','travelled_more_than_5mins_for_offer']\n",
    "train_x = train_x.drop(cols,axis=1)\n",
    "test = test.drop(cols,axis=1)\n",
    "print(train_x.shape,test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "37d5bc34",
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "# train_x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5800c35",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(train_x,train_y,test_size=0.20,random_state=42)"
   ]
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   "cell_type": "code",
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   "id": "f89f9c34",
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    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer,KNNImputer\n",
    "\n",
    "col = ['no_visited_Cold drinks',\n",
    "       'Restaur_spend_less_than20',\n",
    "       'no_visited_bars',\n",
    "       'no_Take-aways',\n",
    "       'Restaur_spend_greater_than20']\n",
    "\n",
    "imputer = SimpleImputer(strategy='most_frequent')\n",
    "\n",
    "x_train[col] = imputer.fit_transform(x_train[col])\n",
    "x_test[col] = imputer.transform(x_test[col])\n",
    "test[col] = imputer.transform(test[col])\n",
    "train_x[col] = imputer.fit_transform(train_x[col])\n",
    "\n",
    "cols = ['offer expiration','income_range','no_visited_Cold drinks','travelled_more_than_15mins_for_offer',\n",
    "        'Restaur_spend_less_than20','Marital Status', 'restaurant type', 'age', 'Prefer western over chinese',\n",
    "        'travelled_more_than_25mins_for_offer', 'no_visited_bars', 'gender', 'restuarant_same_direction_house',\n",
    "        'Cooks regularly', 'Customer type', 'Qualification', 'is foodie', 'no_Take-aways',\n",
    "        'restuarant_opposite_direction_house', 'has Children', 'visit restaurant with rating (avg)', \n",
    "        'temperature','Restaur_spend_greater_than20', 'Travel Time', 'Climate','drop location', 'Prefer home food']\n",
    "\n",
    "x_train = pd.DataFrame(x_train, columns = cols)\n",
    "x_test = pd.DataFrame(x_test, columns = cols)\n",
    "test = pd.DataFrame(test, columns = cols)\n",
    "train_x = pd.DataFrame(train_x, columns = cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "50798988",
   "metadata": {
    "execution": {
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     "status": "completed"
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   },
   "outputs": [],
   "source": [
    "#x_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "90319d2d",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "le = LabelEncoder()\n",
    "y_train = le.fit_transform(y_train)\n",
    "y_test = le.fit_transform(y_test)\n",
    "train_y = le.fit_transform(train_y)\n",
    "\n",
    "cols = x_train.columns\n",
    "\n",
    "ordinal = ['income_range',\n",
    "           'no_visited_Cold drinks',\n",
    "           'Restaur_spend_less_than20',\n",
    "           'age',\n",
    "           'no_visited_bars',\n",
    "           'no_Take-aways',\n",
    "           'visit restaurant with rating (avg)',\n",
    "           'temperature',\n",
    "           'Restaur_spend_greater_than20',\n",
    "           'Travel Time'] \n",
    "\n",
    "nominal = ['offer expiration',\n",
    "           'Marital Status',\n",
    "           'restaurant type',\n",
    "           'gender',\n",
    "           'Customer type',\n",
    "           'Qualification',\n",
    "           'is foodie',\n",
    "           'Climate',\n",
    "           'drop location',\n",
    "          'Job/Job Industry']\n",
    "\n",
    "rest = ['travelled_more_than_15mins_for_offer',\n",
    "        'Prefer western over chinese',\n",
    "        'travelled_more_than_25mins_for_offer',\n",
    "        'restuarant_same_direction_house',\n",
    "        'Cooks regularly',\n",
    "        'is foodie',\n",
    "        'restuarant_opposite_direction_house',\n",
    "        'has Children',\n",
    "        'Prefer home food',\n",
    "        'income_range',\n",
    "        'no_visited_Cold drinks',\n",
    "        'Restaur_spend_less_than20',\n",
    "        'age',\n",
    "        'no_visited_bars',\n",
    "        'no_Take-aways',\n",
    "        'visit restaurant with rating (avg)',\n",
    "        'temperature',\n",
    "        'Restaur_spend_greater_than20',\n",
    "        'Travel Time']\n",
    "        \n",
    "income = {'Less than ₹12500':1,'₹12500 - ₹24999':2,'₹25000 - ₹37499':3,'₹37500 - ₹49999':4,'₹50000 - ₹62499':5,'₹62500 - ₹74999':6,'₹75000 - ₹87499':7,'₹87500 - ₹99999':8,'₹100000 or More':9}\n",
    "gen = {'never':1,'less1':2,'1~3':3,'4~8':4,'gt8':5}\n",
    "age = {'below21':1,'21':2,'26':3,'31':4,'36':5,'41':6,'46':7,'50plus':8}\n",
    "rating = {0:1,1:2,2:3,3:4,4:5}\n",
    "tem = {40:1,67:2,89:3}\n",
    "travel = {7:1,10:2,14:3,18:4,22:5}\n",
    "\n",
    "x_train = x_train.replace({'income_range':income,\n",
    "                           'no_visited_Cold drinks':gen,\n",
    "                           'Restaur_spend_less_than20':gen,\n",
    "                           'age':age,\n",
    "                           'no_visited_bars':gen,\n",
    "                           'no_Take-aways':gen,\n",
    "                           'visit restaurant with rating (avg)':rating,\n",
    "                           'temperature':tem,\n",
    "                           'Restaur_spend_greater_than20':gen,\n",
    "                           'Travel Time':travel})\n",
    "\n",
    "train_x = train_x.replace({'income_range':income,\n",
    "                           'no_visited_Cold drinks':gen,\n",
    "                           'Restaur_spend_less_than20':gen,\n",
    "                           'age':age,\n",
    "                           'no_visited_bars':gen,\n",
    "                           'no_Take-aways':gen,\n",
    "                           'visit restaurant with rating (avg)':rating,\n",
    "                           'temperature':tem,\n",
    "                           'Restaur_spend_greater_than20':gen,\n",
    "                           'Travel Time':travel})\n",
    "\n",
    "x_test = x_test.replace({'income_range':income,\n",
    "                           'no_visited_Cold drinks':gen,\n",
    "                           'Restaur_spend_less_than20':gen,\n",
    "                           'age':age,\n",
    "                           'no_visited_bars':gen,\n",
    "                           'no_Take-aways':gen,\n",
    "                           'visit restaurant with rating (avg)':rating,\n",
    "                           'temperature':tem,\n",
    "                           'Restaur_spend_greater_than20':gen,\n",
    "                           'Travel Time':travel})\n",
    "\n",
    "test = test.replace({'income_range':income,\n",
    "                           'no_visited_Cold drinks':gen,\n",
    "                           'Restaur_spend_less_than20':gen,\n",
    "                           'age':age,\n",
    "                           'no_visited_bars':gen,\n",
    "                           'no_Take-aways':gen,\n",
    "                           'visit restaurant with rating (avg)':rating,\n",
    "                           'temperature':tem,\n",
    "                           'Restaur_spend_greater_than20':gen,\n",
    "                           'Travel Time':travel})\n",
    "\n",
    "transformer = ColumnTransformer([('nominal',OneHotEncoder(),cols)])\n",
    "\n",
    "\n",
    "x_train = transformer.fit_transform(x_train)\n",
    "train_x = transformer.fit_transform(train_x)\n",
    "x_test = transformer.transform(x_test)\n",
    "test = transformer.transform(test)\n",
    "\n",
    "# encode = OneHotEncoder()\n",
    "# x_train = encode.fit_transform(x_train)\n",
    "# train_x = encode.fit_transform(train_x)\n",
    "# x_test = encode.transform(x_test)\n",
    "# test = encode.transform(test)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
   "id": "5178a355",
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    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fd9610aa",
   "metadata": {
    "execution": {
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    },
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   },
   "outputs": [],
   "source": [
    "# df=pd.DataFrame(train_x)\n",
    "# df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6082e8ca",
   "metadata": {
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   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score,confusion_matrix\n",
    "\n",
    "from sklearn.model_selection import cross_val_score,GridSearchCV,KFold,StratifiedKFold\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest,chi2\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingClassifier,RandomForestClassifier,BaggingClassifier,AdaBoostClassifier,VotingClassifier\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression,LogisticRegressionCV,RidgeClassifier,RidgeClassifierCV,Perceptron\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier,RadiusNeighborsClassifier\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB,CategoricalNB,BernoulliNB\n",
    "\n",
    "from sklearn.svm import SVC,NuSVC,LinearSVC\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fd936d35",
   "metadata": {
    "execution": {
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     "start_time": "2022-12-18T17:00:02.792050",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# skb = SelectKBest(chi2,k=40)\n",
    "# x_train = skb.fit_transform(x_train,y_train)\n",
    "# train_x = skb.fit_transform(train_x,train_y)\n",
    "# x_test = skb.transform(x_test)\n",
    "# test = skb.transform(test)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 15,
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    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:02.832481Z",
     "iopub.status.busy": "2022-12-18T17:00:02.831596Z",
     "iopub.status.idle": "2022-12-18T17:00:02.837360Z",
     "shell.execute_reply": "2022-12-18T17:00:02.836145Z"
    },
    "papermill": {
     "duration": 0.019635,
     "end_time": "2022-12-18T17:00:02.840206",
     "exception": false,
     "start_time": "2022-12-18T17:00:02.820571",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model = xgb.XGBClassifier()\n",
    "# model.get_params()\n",
    "\n",
    "# model.fit(x_train,y_train)\n",
    "# y_pred = model.predict(x_test)\n",
    "# print(f1_score(y_test,y_pred))\n",
    "# confusion_matrix(y_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "19305aac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:02.861762Z",
     "iopub.status.busy": "2022-12-18T17:00:02.860785Z",
     "iopub.status.idle": "2022-12-18T17:00:02.867014Z",
     "shell.execute_reply": "2022-12-18T17:00:02.866106Z"
    },
    "papermill": {
     "duration": 0.019878,
     "end_time": "2022-12-18T17:00:02.869511",
     "exception": false,
     "start_time": "2022-12-18T17:00:02.849633",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# grid = {'objective': ['binary:logistic'],\n",
    "#  'use_label_encoder': [True,False],\n",
    "#  'booster': ['gbtree','gblinear','dart',None],\n",
    "#  'colsample_bylevel': [None],\n",
    "#  'colsample_bynode': [None],\n",
    "#  'colsample_bytree': [None],\n",
    "#  'early_stopping_rounds': [None],\n",
    "#  'enable_categorical': [True,False],\n",
    "#  'eval_metric': [None],\n",
    "#  'gamma': [None],\n",
    "#  'gpu_id': [None],\n",
    "#  'grow_policy': [0,1,None],\n",
    "#  'importance_type': [None],\n",
    "#  'interaction_constraints': [None],\n",
    "#  'learning_rate': [0.3,0.5,None],\n",
    "#  'max_bin': [None],\n",
    "#  'max_cat_to_onehot': [None],\n",
    "#  'max_delta_step': [None],\n",
    "#  'max_depth': [None],\n",
    "#  'max_leaves': [None],\n",
    "#  'min_child_weight': [None],\n",
    "#  'monotone_constraints': [None],\n",
    "#  'n_estimators': [50,100,120],\n",
    "#  'num_parallel_tree': [None],\n",
    "#  'predictor': [None],\n",
    "#  'reg_alpha': [1,0.1,None],\n",
    "#  'reg_lambda': [1,0.1,None],\n",
    "#  'sampling_method': [None],\n",
    "#  'scale_pos_weight': [None],\n",
    "#  'subsample': [None],\n",
    "#  'tree_method': ['auto',None],\n",
    "#  'validate_parameters': [None]}\n",
    "\n",
    "# best = GridSearchCV(xgb.XGBClassifier(random_state=42),grid, scoring='f1_micro', cv=3,n_jobs = -1, verbose = 0)\n",
    "# best.fit(x_train,y_train)\n",
    "# best.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2d95dac0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:02.890262Z",
     "iopub.status.busy": "2022-12-18T17:00:02.889331Z",
     "iopub.status.idle": "2022-12-18T17:00:02.894824Z",
     "shell.execute_reply": "2022-12-18T17:00:02.893786Z"
    },
    "papermill": {
     "duration": 0.019118,
     "end_time": "2022-12-18T17:00:02.897618",
     "exception": false,
     "start_time": "2022-12-18T17:00:02.878500",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model = RadiusNeighborsClassifier(radius=20)\n",
    "# model.fit(x_train,y_train)\n",
    "# y_pred = model.predict(x_test)\n",
    "# print(f1_score(y_test,y_pred))\n",
    "# confusion_matrix(y_test,y_pred)\n",
    "\n",
    "# 0.7127631920977385"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a2d77e5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:02.919010Z",
     "iopub.status.busy": "2022-12-18T17:00:02.918085Z",
     "iopub.status.idle": "2022-12-18T17:00:02.923642Z",
     "shell.execute_reply": "2022-12-18T17:00:02.922734Z"
    },
    "papermill": {
     "duration": 0.019432,
     "end_time": "2022-12-18T17:00:02.926313",
     "exception": false,
     "start_time": "2022-12-18T17:00:02.906881",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model = RandomForestClassifier(bootstrap= True, \n",
    "#                                criterion= 'gini', \n",
    "#                                max_depth= None, \n",
    "#                                max_features= None, \n",
    "#                                min_samples_leaf= 4, \n",
    "#                                min_samples_split= 7, \n",
    "#                                n_estimators= 200)\n",
    "# model.fit(x_train,y_train)\n",
    "# y_pred = model.predict(x_test)\n",
    "# print(f1_score(y_test,y_pred))\n",
    "# confusion_matrix(y_test,y_pred)\n",
    "\n",
    "# 7143289912147834"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1806d80c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:02.947942Z",
     "iopub.status.busy": "2022-12-18T17:00:02.947011Z",
     "iopub.status.idle": "2022-12-18T17:00:02.952400Z",
     "shell.execute_reply": "2022-12-18T17:00:02.951489Z"
    },
    "papermill": {
     "duration": 0.018984,
     "end_time": "2022-12-18T17:00:02.954852",
     "exception": false,
     "start_time": "2022-12-18T17:00:02.935868",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model = KNeighborsClassifier(algorithm='ball_tree',leaf_size=25,metric='hamming',\n",
    "#         metric_params= None, n_neighbors=60,p=2,weights='distance')\n",
    "\n",
    "# model.fit(x_train,y_train)\n",
    "# y_pred = model.predict(x_test)\n",
    "# print(f1_score(y_test,y_pred))\n",
    "# confusion_matrix(y_test,y_pred)\n",
    "\n",
    "# 0.7171995589856671"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0e74cf08",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:02.975217Z",
     "iopub.status.busy": "2022-12-18T17:00:02.974697Z",
     "iopub.status.idle": "2022-12-18T17:00:02.979991Z",
     "shell.execute_reply": "2022-12-18T17:00:02.978689Z"
    },
    "papermill": {
     "duration": 0.018702,
     "end_time": "2022-12-18T17:00:02.982577",
     "exception": false,
     "start_time": "2022-12-18T17:00:02.963875",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model=SVC(C = 3,\n",
    "#           class_weight= None,\n",
    "#           degree= 1,\n",
    "#           gamma= 'scale',\n",
    "#           kernel= 'rbf',\n",
    "#           probability= True,\n",
    "#           shrinking= True,\n",
    "#           random_state=42)\n",
    "\n",
    "# model.fit(x_train,y_train)\n",
    "# y_pred = model.predict(x_test)\n",
    "# print(f1_score(y_test,y_pred))\n",
    "# confusion_matrix(y_test,y_pred)\n",
    "\n",
    "# 0.7134946079860098"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4f65ad79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:03.004238Z",
     "iopub.status.busy": "2022-12-18T17:00:03.002823Z",
     "iopub.status.idle": "2022-12-18T17:00:04.267420Z",
     "shell.execute_reply": "2022-12-18T17:00:04.265366Z"
    },
    "papermill": {
     "duration": 1.278636,
     "end_time": "2022-12-18T17:00:04.270765",
     "exception": false,
     "start_time": "2022-12-18T17:00:02.992129",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.712294828120161\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 397,  708],\n",
       "       [ 221, 1150]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=xgb.XGBClassifier(learning_rate=0.013,booster='gbtree')\n",
    "model.fit(x_train,y_train)\n",
    "y_pred = model.predict(x_test)\n",
    "print(f1_score(y_test,y_pred))\n",
    "confusion_matrix(y_test,y_pred)\n",
    "\n",
    "# 0.673778389538885"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "07b9eb28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:04.291897Z",
     "iopub.status.busy": "2022-12-18T17:00:04.291073Z",
     "iopub.status.idle": "2022-12-18T17:00:04.296784Z",
     "shell.execute_reply": "2022-12-18T17:00:04.295664Z"
    },
    "papermill": {
     "duration": 0.019268,
     "end_time": "2022-12-18T17:00:04.299216",
     "exception": false,
     "start_time": "2022-12-18T17:00:04.279948",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model = AdaBoostClassifier(base_estimator=RandomForestClassifier(bootstrap= True,criterion= 'gini', \n",
    "#                     max_depth= None, max_features= None, min_samples_leaf= 4,min_samples_split= 7, \n",
    "#                     n_estimators= 200),n_estimators=10)\n",
    "# model.fit(x_train,y_train)\n",
    "# y_pred = model.predict(x_test)\n",
    "# print(f1_score(y_test,y_pred))\n",
    "# confusion_matrix(y_test,y_pred)\n",
    "\n",
    "# 0.6888604353393085"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8fd079ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:04.319994Z",
     "iopub.status.busy": "2022-12-18T17:00:04.319499Z",
     "iopub.status.idle": "2022-12-18T17:00:04.325121Z",
     "shell.execute_reply": "2022-12-18T17:00:04.323698Z"
    },
    "papermill": {
     "duration": 0.019156,
     "end_time": "2022-12-18T17:00:04.327899",
     "exception": false,
     "start_time": "2022-12-18T17:00:04.308743",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model = GradientBoostingClassifier(random_state=42,loss='deviance',learning_rate = 0.08,criterion='friedman_mse',\n",
    "#         min_samples_split= 0.025, min_samples_leaf= 0.1,max_depth=2,max_features=None,warm_start=True)\n",
    "# model.fit(x_train,y_train)\n",
    "# y_pred = model.predict(x_test)\n",
    "# print(f1_score(y_test,y_pred))\n",
    "# confusion_matrix(y_test,y_pred)\n",
    "\n",
    "#0.7053231939163499"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a9c7af8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:04.349463Z",
     "iopub.status.busy": "2022-12-18T17:00:04.348629Z",
     "iopub.status.idle": "2022-12-18T17:00:04.354269Z",
     "shell.execute_reply": "2022-12-18T17:00:04.353221Z"
    },
    "papermill": {
     "duration": 0.019158,
     "end_time": "2022-12-18T17:00:04.356616",
     "exception": false,
     "start_time": "2022-12-18T17:00:04.337458",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model = AdaBoostClassifier(base_estimator=SVC(C = 3,\n",
    "#           class_weight= None,\n",
    "#           degree= 1,\n",
    "#           gamma= 'scale',\n",
    "#           kernel= 'rbf',\n",
    "#           probability= True,\n",
    "#           shrinking= True,\n",
    "#           random_state=42),n_estimators=10)\n",
    "# model.fit(x_train,y_train)\n",
    "# y_pred = model.predict(x_test)\n",
    "# print(f1_score(y_test,y_pred))\n",
    "# confusion_matrix(y_test,y_pred)\n",
    "\n",
    "# 0.6928731224033238"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a65507fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:04.378079Z",
     "iopub.status.busy": "2022-12-18T17:00:04.377298Z",
     "iopub.status.idle": "2022-12-18T17:00:04.383021Z",
     "shell.execute_reply": "2022-12-18T17:00:04.381949Z"
    },
    "papermill": {
     "duration": 0.019578,
     "end_time": "2022-12-18T17:00:04.385518",
     "exception": false,
     "start_time": "2022-12-18T17:00:04.365940",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# CategoricalNB().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "28f72cac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:04.407388Z",
     "iopub.status.busy": "2022-12-18T17:00:04.406877Z",
     "iopub.status.idle": "2022-12-18T17:00:04.411935Z",
     "shell.execute_reply": "2022-12-18T17:00:04.410574Z"
    },
    "papermill": {
     "duration": 0.019642,
     "end_time": "2022-12-18T17:00:04.414629",
     "exception": false,
     "start_time": "2022-12-18T17:00:04.394987",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model = CategoricalNB()\n",
    "# model.fit(x_train.toarray(),y_train)\n",
    "# y_pred = model.predict(x_test.toarray())\n",
    "# print(f1_score(y_test,y_pred))\n",
    "# confusion_matrix(y_test,y_pred)\n",
    "\n",
    "# 0.6784776902887139"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "560e1e52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:04.435697Z",
     "iopub.status.busy": "2022-12-18T17:00:04.435182Z",
     "iopub.status.idle": "2022-12-18T17:00:04.441124Z",
     "shell.execute_reply": "2022-12-18T17:00:04.439676Z"
    },
    "papermill": {
     "duration": 0.019423,
     "end_time": "2022-12-18T17:00:04.443628",
     "exception": false,
     "start_time": "2022-12-18T17:00:04.424205",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model = BaggingClassifier(base_estimator=RandomForestClassifier(bootstrap= True, \n",
    "#                                criterion= 'gini', \n",
    "#                                max_depth= None, \n",
    "#                                max_features= None, \n",
    "#                                min_samples_leaf= 4, \n",
    "#                                min_samples_split= 7, \n",
    "#                                n_estimators= 200))\n",
    "# model.fit(x_train,y_train)\n",
    "# y_pred = model.predict(x_test)\n",
    "# print(f1_score(y_test,y_pred))\n",
    "# confusion_matrix(y_test,y_pred)\n",
    "\n",
    "# 0.6121687196715192"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a08a63e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:04.465594Z",
     "iopub.status.busy": "2022-12-18T17:00:04.464236Z",
     "iopub.status.idle": "2022-12-18T17:00:04.470367Z",
     "shell.execute_reply": "2022-12-18T17:00:04.469222Z"
    },
    "papermill": {
     "duration": 0.01986,
     "end_time": "2022-12-18T17:00:04.473055",
     "exception": false,
     "start_time": "2022-12-18T17:00:04.453195",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# {'activation': ['relu'],\n",
    "#  'alpha': [0.0001],\n",
    "#  'batch_size': [100],\n",
    "#  'early_stopping': [True],\n",
    "#  'solver': ['adam'],\n",
    "#  'warm_start': [True],       \n",
    "#  'hidden_layer_sizes': [(500,300,200,50)],\n",
    "#  'learning_rate': ['constant'],\n",
    "#  'learning_rate_init': [0.01],\n",
    "#  'max_iter': [200],\n",
    "#  'random_state': [42]}\n",
    "\n",
    "# 0.7119\n",
    "\n",
    "# model = MLPClassifier(activation='relu',alpha=0.0001,batch_size=100,early_stopping=True, solver='adam',\n",
    "#                       warm_start=True,learning_rate='constant',learning_rate_init=0.01,max_iter=200,\n",
    "#                      random_state=42,hidden_layer_sizes=(300,200,100,2))\n",
    "# model.fit(x_train,y_train)\n",
    "# y_pred = model.predict(x_test)\n",
    "# print(f1_score(y_test,y_pred))\n",
    "# confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "337406a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:04.494070Z",
     "iopub.status.busy": "2022-12-18T17:00:04.493517Z",
     "iopub.status.idle": "2022-12-18T17:00:04.500156Z",
     "shell.execute_reply": "2022-12-18T17:00:04.498824Z"
    },
    "papermill": {
     "duration": 0.020173,
     "end_time": "2022-12-18T17:00:04.502740",
     "exception": false,
     "start_time": "2022-12-18T17:00:04.482567",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# clf1 = BaggingClassifier(base_estimator=RandomForestClassifier(bootstrap= True, \n",
    "#                                criterion= 'gini', \n",
    "#                                max_depth= None, \n",
    "#                                max_features= None, \n",
    "#                                min_samples_leaf= 4, \n",
    "#                                min_samples_split= 7, \n",
    "#                                n_estimators= 200))\n",
    "\n",
    "# clf2 = AdaBoostClassifier(base_estimator=RandomForestClassifier(bootstrap= True,criterion= 'gini', \n",
    "#                     max_depth= None, max_features= None, min_samples_leaf= 4,min_samples_split= 7, \n",
    "#                     n_estimators= 200),n_estimators=10)\n",
    "\n",
    "# clf3 = GradientBoostingClassifier(random_state=42,loss='deviance',learning_rate = 0.08,criterion='friedman_mse',\n",
    "#          min_samples_split= 0.025, min_samples_leaf= 0.1,max_depth=2,max_features=None,warm_start=True)\n",
    "\n",
    "# clf4 = MLPClassifier(activation='relu',alpha=0.0001,batch_size=100,early_stopping=True, solver='adam',\n",
    "#                       warm_start=True,learning_rate='constant',learning_rate_init=0.01,max_iter=200,\n",
    "#                      random_state=42,hidden_layer_sizes=(1000,500,200,50))\n",
    "\n",
    "# model = VotingClassifier(estimators=[('1',clf1),('2',clf2),('3',clf3),('4',clf4)],voting='soft')\n",
    "\n",
    "# model.fit(x_train,y_train)\n",
    "# y_pred = model.predict(x_test)\n",
    "# print(f1_score(y_test,y_pred))\n",
    "# confusion_matrix(y_test,y_pred)\n",
    "\n",
    "#0.7061056627649478"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ee60bc57",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:04.523744Z",
     "iopub.status.busy": "2022-12-18T17:00:04.523266Z",
     "iopub.status.idle": "2022-12-18T17:00:04.528665Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from scipy.stats import mode\n",
    "# y_pred = [mode([i,j,k])[0][0] for i,j,k in zip(y_pred1,y_pred2,y_pred3)]\n",
    "# f1_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dab18405",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:04.552584Z",
     "iopub.status.busy": "2022-12-18T17:00:04.552081Z",
     "iopub.status.idle": "2022-12-18T17:00:04.557853Z",
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     "start_time": "2022-12-18T17:00:04.540559",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# temp=DecisionTreeClassifier()\n",
    "# temp.fit(x_train,y_train)\n",
    "# y_pred = temp.predict(x_test)\n",
    "# print(f1_score(y_test,y_pred))\n",
    "# confusion_matrix(y_test,y_pred)\n",
    "\n",
    "# 0.5773348098929495"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "97938205",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-18T17:00:04.581782Z",
     "iopub.status.busy": "2022-12-18T17:00:04.581236Z",
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    },
    "papermill": {
     "duration": 1389.034833,
     "end_time": "2022-12-18T17:23:13.605003",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "clf1 = BaggingClassifier(base_estimator=RandomForestClassifier(bootstrap= True, \n",
    "                               criterion= 'gini', \n",
    "                               max_depth= None, \n",
    "                               max_features= None, \n",
    "                               min_samples_leaf= 4, \n",
    "                               min_samples_split= 7, \n",
    "                               n_estimators= 200))\n",
    "\n",
    "clf2 = AdaBoostClassifier(base_estimator=RandomForestClassifier(bootstrap= True,criterion= 'gini', \n",
    "                    max_depth= None, max_features= None, min_samples_leaf= 4,min_samples_split= 7, \n",
    "                    n_estimators= 200),n_estimators=10)\n",
    "\n",
    "# clf3 = GradientBoostingClassifier(random_state=42,loss='deviance',learning_rate = 0.08,criterion='friedman_mse',\n",
    "#          min_samples_split= 0.025, min_samples_leaf= 0.1,max_depth=2,max_features=None,warm_start=True)\n",
    "\n",
    "clf3= xgb.XGBClassifier(learning_rate=0.013,booster='gbtree')\n",
    "\n",
    "clf4 = MLPClassifier(activation='relu',alpha=0.0001,batch_size=100,early_stopping=True, solver='adam',\n",
    "                      warm_start=True,learning_rate='constant',learning_rate_init=0.01,max_iter=200,\n",
    "                     random_state=42,hidden_layer_sizes=(500,100,200,5))\n",
    "\n",
    "\n",
    "\n",
    "temp = VotingClassifier(estimators=[('1',clf1),('2',clf2),('3',clf3),('4',clf4)],voting='soft')\n",
    "# temp = \n",
    "temp.fit(train_x, train_y)\n",
    "test_pred = temp.predict(test)\n",
    "\n",
    "ids=[id for id in range(len(list(test)))]\n",
    "s=[]\n",
    "for i in test_pred:\n",
    "    if i==1:\n",
    "        s+=['Yes']\n",
    "    else:\n",
    "        s+=['No']\n",
    "\n",
    "\n",
    "submit=pd.DataFrame({'id':ids, 'Offer Accepted':s})\n",
    "submit.to_csv('submission.csv',index=False)"
   ]
  }
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