Delete model.py

model.py

@@ -1,104 +0,0 @@
-import numpy as np
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn import svm
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.ensemble import GradientBoostingClassifier
-from xgboost import XGBClassifier
-from sklearn.metrics import accuracy_score
-import joblib
-import pickle
-import onnx
-import random
-
-
-df=pd.read_csv("Placement (2).csv")
-df.head()
-
-df=df.drop(columns=["sl_no","stream","ssc_p","ssc_b","hsc_p","hsc_b","etest_p"])
-df['internship'] = df['internship'].map({'Yes':random.randint(0,5),'No':0})
-df['status'] = df['status'].map({'Placed':1,'Not Placed':0})
-df.head()
-X_fullstk= df.drop(['status','management','leadership','communication','sales'],axis=1)
-y= df['status']
-X_prodengg=df.drop(['status','DSA','java','communication','sales'],axis=1)
-X_mkt=df.drop(['status','management','leadership','DSA','java'],axis=1)
-X_train_fullstk,X_test_fullstk,y_train,y_test=train_test_split(X_fullstk,y,test_size=0.20,random_state=42)
-X_train_prodengg,X_test_prodengg,y_train,y_test=train_test_split(X_prodengg,y,test_size=0.20,random_state=42)
-X_train_mkt,X_test_mkt,y_train,y_test=train_test_split(X_mkt,y,test_size=0.20,random_state=42)
-rf_fullstk = RandomForestClassifier()
-rf_fullstk.fit(X_train_fullstk,y_train)
-
-rf_prodengg=RandomForestClassifier()
-rf_prodengg.fit(X_train_prodengg,y_train)
-
-rf_mkt=RandomForestClassifier()
-rf_mkt.fit(X_train_mkt,y_train)
-
-y_pred_full = rf_fullstk.predict(X_test_fullstk)
-y_pred_prodengg = rf_prodengg.predict(X_test_prodengg)
-y_pred_mkt = rf_mkt.predict(X_test_mkt)
-score1=accuracy_score(y_test,y_pred_full)
-score2=accuracy_score(y_test,y_pred_prodengg)
-score3=accuracy_score(y_test,y_pred_mkt)
-rf_fullstk.fit(X_fullstk,y)
-rf_mkt.fit(X_mkt,y)
-rf_prodengg.fit(X_prodengg,y)
-
-new_data_fullstk = pd.DataFrame({
-    'degree_p':75,
-    'internship':1,
-    'DSA':1,
-    'java':0,
-},index=[0])
-
-new_data_prodeng = pd.DataFrame({
-    'degree_p':75,
-    'internship':0,
-    'management':1,
-    'leadership':0,
-},index=[0])
-
-new_data_mkt = pd.DataFrame({
-    'degree_p':75,
-    'internship':0,
-    'communication':0,
-    'sales':1,
-},index=[0])
-
-(rf_fullstk.feature_importances_)
-(rf_mkt.feature_importances_)
-
-p_fstk=rf_fullstk.predict(new_data_fullstk)
-prob_fstk=rf_fullstk.predict_proba(new_data_fullstk)
-if p_fstk==1:
-    print('Placed')
-    print(f"You will be placed with probability of {prob_fstk[0][1]:.2f}")
-else:
-    print("Not-placed")
-    
-p_prodeng=rf_prodengg.predict(new_data_prodeng)
-prob_prdeng=rf_prodengg.predict_proba(new_data_prodeng)
-if p_prodeng==1:
-    print('Placed')
-    print(f"You will be placed with probability of {prob_prdeng[0][1]:.2f}")
-else:
-    print("Not-placed")
-
-p_mkt=rf_mkt.predict(new_data_mkt)
-prob_mkt=rf_mkt.predict_proba(new_data_mkt)
-if p_mkt==1:
-    print('Placed')
-    print(f"You will be placed with probability of {prob_mkt[0][1]:.2f}")
-else:
-    print("Not-placed")
-    
-with open('rf_hacathon_fullstk.pkl', 'wb') as f1:
-    pickle.dump(rf_fullstk, f1)
-with open('rf_hacathon_prodengg.pkl', 'wb') as f2:
-    pickle.dump(rf_prodengg, f2)
-with open('rf_hacathon_mkt.pkl', 'wb') as f3:
-    pickle.dump(rf_mkt, f3)