Carls Car Purchase Prediction ANN.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


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car_df = pd.read_csv('Car_Purchasing_Data.csv', encoding = 'ISO-8859-1')


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car_df.head(10)


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car_df.tail(10)


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sns.pairplot(car_df)


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X = car_df.drop(['Customer Name', 'Customer e-mail', 'Country', 'Car Purchase Amount'], axis = 1)


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y = car_df['Car Purchase Amount']


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y


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X.shape


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y.shape


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from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)


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X_scaled


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scaler.data_max_


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scaler.data_min_


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y = y.values.reshape(-1,1)


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y_scaled = scaler.fit_transform(y)


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y_scaled


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from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled, test_size = 0.25)


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X_scaled.shape


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X_train.shape


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X_test.shape


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import tensorflow.keras
from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(25, input_dim = 5, activation = 'relu'))
model.add(Dense(25, activation = 'relu'))
model.add(Dense(1, activation = 'linear'))


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model.summary()


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model.compile(optimizer = 'adam', loss = 'mean_squared_error')


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epochs_hist = model.fit(X_train, y_train, epochs = 100, batch_size = 50, verbose = 1, validation_split = 0.2)


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epochs_hist.history.keys()


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plt.plot(epochs_hist.history['loss'])
plt.plot(epochs_hist.history['val_loss'])
plt.title('Model Loss Progress During Training')
plt.ylabel(' Training and Validation Loss')
plt.xlabel('Epoch number')
plt.legend(['Training Loss', 'Validation Loss'])


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# Gender, Age, Annual Salary, Credit Card Debt, Net Worth
X_test = np.array([[1, 50, 50000, 10000, 10000]])
y_predict = model.predict(X_test)


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print('Expected Purchase Amount', y_predict)


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