Training Machine Learning Algorithm

CO2 emissions canada 2023 Prediction

Working on a regression problem using various machine learning models to predict " CO2 emissions canada 2023 " based on features such as engine size, fuel consumption, and cylinders. Here's a breakdown of the code you provided:

1. Importing libraries:

   • Pandas: for data manipulation and analysis
   • NumPy: for numerical operations
   • Matplotlib: for data visualization
   • Seaborn: for statistical data visualization
   • Various modules from scikit-learn: for machine learning algorithms and evaluation metrics

2. Loading the dataset:

   • The dataset is loaded from a CSV file called 'FuelConsumption2023.csv' using pd.read_csv().
   • The columns of interest are selected and stored in the 'df' DataFrame.

3. Data exploration:

   • Dataset dimensions are printed using df.shape.
   • The first few rows of the dataset are displayed using df.head().
   • Data types of each column are printed using df.dtypes.
   • The number of missing values in each column is displayed using df.isnull().sum().
   • Summary statistics of numerical columns are printed using df.describe().
   • Unique values in categorical columns (if any) are displayed using a loop.

4. Data preprocessing:

   • Missing values are dropped from the dataset using df.dropna().
   • One-hot encoding is performed on categorical columns (if any) using pd.get_dummies().
   • Feature scaling or normalization is performed on the encoded dataset using MinMaxScaler().

5. Data visualization:

   • The distribution of numeric columns is visualized using histograms and KDE plots with the help of sns.histplot().
   • Relationships between variables are visualized using scatter plots with regression lines using sns.regplot().

6. Model training and evaluation:

   • Selected features and target variable are assigned to 'X' and 'y', respectively.
   • The data is split into training and testing sets using train_test_split().
   • Several regression models are trained on the training set and evaluated on the testing set:
     • Linear Regression (LR)
     • Support Vector Regression (SVR)
     • Multilayer Perceptron (MLP)
     • Decision Tree (Regression)
     • Random Forest
     • Gradient Boosting (GB)
     • K-Nearest Neighbors (KNN)
   • Evaluation metrics (MSE and R-squared) are calculated using mean_squared_error() and r2_score().
   • Scatter plots of actual vs predicted values are plotted using plt.scatter().

7. Model tuning:

   • Hyperparameter grids for each model are defined.
   • Grid search is performed for each model using GridSearchCV() to find the best hyperparameters.
   • Tuned models are evaluated and scatter plots of actual vs predicted values are plotted.