I am doing wind forecasting modeling using SVR model and I wanted to compare the actual wind speed data and the predicted data. 
I am not sure what I did wrong. I have pre-process the datasets and normalize it.
>
> # Define column names column_names = ['datetime', 'place', 'city', 'state', 'temperature', 'pressure', 'humidity', 'wind_speed', 'gust',
> 'wind_chill', 'feels_like_temperature']
>
> # Extract feature and target X = df.drop('wind_speed', axis=1) y = df['wind_speed']
>
> # Train - test split X_train , X_test , y_train , y_test = train_test_split (X , y , test_size =0.3 , random_state =42)
>
> # Normalize Data scaler = MinMaxScaler () df_normalized = scaler.fit_transform(df) df_normalized = pd.DataFrame(df_normalized,
> columns=df.columns) X_train = scaler.fit_transform (X_train) X_test =
> scaler.transform(X_test)
>
> # Train using Linear Regression model_lr = LinearRegression() model_lr.fit(X_train, y_train)
>
> # Predict missing values on test set y_pred_lr = model_lr.predict(X_test)
>
> # Support Vector Regression (SVR) Model svr_model = SVR(kernel='rbf', C=100, epsilon= 0.1) svr_model.fit(X_train, y_train)
>
> # Predicting and Evaluate SVR results y_pred_svr = svr_model.predict(X_test) mse_svr = mean_squared_error(y_test,
> y_pred_svr) r2_svr = r2_score(y_test, y_pred_svr) print(mse_svr)
> print(r2_svr)
>
> # Visualising SVR results plt.scatter(y_test, y_pred_svr, color='blue', label='Predicted') plt.scatter(y_test, y_test,
> color='red', label='Actual') plt.plot([min(y_test), max(y_test)],
> [min(y_test), max(y_test)], color='black', linestyle='--')
> plt.xlabel('Actual Wind Speed') plt.ylabel('Predicted Wind Speed')
> plt.title('SVR Predictions vs. Actual Values') plt.legend() plt.show()
let me know what i did wrong
