Create salary_prediction

salary_prediction

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+    
+    ##### RUN SCRIPT #######
+
+import pandas as pd
+from sklearn.impute import SimpleImputer
+import numpy as np
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import OneHotEncoder,LabelEncoder,StandardScaler,PolynomialFeatures
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from joblib import dump
+import os
+
+data = pd.read_csv("src/datasets/Salary Prediction of Data Professions.csv")
+
+X = data.iloc[:, data.columns != data.columns[7]].values
+X = X[:, [i for i in range(X.shape[1]) if i not in [0, 1, 3]]]
+Y = data.iloc[:, 7].values
+print("initial array",X[0])
+
+imputer = SimpleImputer(missing_values=np.nan,strategy='mean')
+imputer.fit(X[:, [3, 5, 6, 7]])
+X[:, [3, 5, 6, 7]] = imputer.transform(X[:, [3, 5, 6, 7]])
+print("after managing missing values",X[0])
+
+X[:, 1] = pd.to_datetime(X[:, 1], format='%d-%m-%Y').astype('int64') // 10**9
+print("after modifying date to timestamp",X[0])
+
+
+ct = ColumnTransformer(transformers=[('encode',OneHotEncoder(),[0])],remainder='passthrough')
+X = ct.fit_transform(X)
+X = np.array(X)
+print("encoding the sex M and F",X[0])
+
+
+label_encoder_2 = LabelEncoder()
+label_encoder_4 = LabelEncoder()
+X[:, 3] = label_encoder_2.fit_transform(X[:, 3])
+X[:, 5] = label_encoder_4.fit_transform(X[:, 5])
+print("encoding position and departement",X[0])
+
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
+
+
+scaler = StandardScaler()
+Y_train = scaler.fit_transform(Y_train.reshape(-1, 1)).ravel()
+Y_test = scaler.transform(Y_test.reshape(-1, 1)).ravel()
+print("Y train",Y_train[0])
+
+polynomial = LinearRegression()
+poly_reg = PolynomialFeatures(degree=2)
+X_train_poly = poly_reg.fit_transform(X_train)
+X_test_poly = poly_reg.fit_transform(X_test)
+
+polynomial_model = polynomial.fit(X_train_poly,Y_train)
+
+poly_train_accuracy = polynomial_model.score(X_train_poly,Y_train)
+poly_test_accuracy = polynomial_model.score(X_test_poly,Y_test)
+
+
+print('poly_train_accuracy',poly_train_accuracy)
+print('poly_test_accuracy',poly_test_accuracy)