ML-Data-Visualization

I learn how to build my first machine learning model in Python using the scikit-learn library.

Documentation

Extract Data

GitHub-Repo-Documentation

Data separation as X and Y

y = data['logS'] print(y)

x = data.drop('logS', axis=1) print(x)

Data splitting

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=100) print(x_train)

Model Building

Linear Regression

from sklearn.linear_model import LinearRegression

lr = LinearRegression() lr.fit(x_train, y_train)

LinearRegression()

Applying the model to make a prediction

y_lr_train_pred = lr.predict(x_train) y_lr_test_pred = lr.predict(y_train)

print(y_lr_train_pred, y_lr_test_pred)

"""Evaluate model preformance """

from sklearn.metrics import mean_squared_error, r2_score

lr_train_mae = mean_squared_error(y_train, y_lr_train_pred) lr_train_r2 = r2_score(y_train, y_lr_train_pred)

lr_test_mae = mean_squared_error(y_train, y_lr_train_pred) lr_test_r2 = r2_score(y_train, y_lr_test_pred)

print('LR MSE (Train):', lr_train_mae) print('LR R2 (Train):', lr_train_r2) print('LR MSE (Train):', lr_test_mae) print('LR R2 (Train):', lr_test_r2)

lr_results = pd.DataFrame(['Linear regression ', lr_train_mae, lr_train_r2, lr_test_mae, lr_test_r2]).transpose() lr_results.colums = ['Method', 'Training MSE', 'Training R2', 'Test MSE', 'Test R2'] print(lr_results)

Data Visualization of prediction results

import matplotlib.pyplot as plt

plt.figure(figsize=(5, 5)) plt.scatter(x=y_train, y=y_lr_train_pred, alpha=0.3)

plt.plot(y_train, p(y_train), '#F8766D') plt.ylabel('Predict LogS') plt.xlabel('Experimental LogS')

Predict LogS