Please write the codes

Objective: The objective Of this project is to explore, analyze, and compare the performance Of at least three different machine learning classifiers or regressors on a medium-sized dataset. Requirements: Dataset Selection: Choose a dataset suitable for classification or regression tasks. Ensure that the dataset has enough instances and features to allow for meaningful analysis. Data Preprocessing: Handle missing values appropriately (e.g., imputation or removal). Encode categorical variables using suitable techniques (e.g., one-hot encoding or label encoding). Normalize or scale numerical features if necessary. Perform exploratory data analysis (EDA) to gain insights into the dataset Feature Selection: Implement a feature selection process to identify relevant features in the dataset. Classifier/Regressor Selection: Select at least three machine learning classifiers or regressors. You can choose from popular algorithms such as Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbors, etc. Hyperparameter Tuning: Perform hyperparameter tuning for each selected model to optimize their performance. 1. Objective Of the Project: 2. Problem Statement: 3. Dataset Details: Dataset Name: [Name Of the Dataset] Source: [Provide the source or origin Of the dataset] Size: [Number Of instances, features, and target variable] Description: [Brief overview Of the dataset, including the nature of features and the target variable] 4. Data Preprocessing: Handling Missing Values: [Describe the approach taken to handle missing data] Encoding Categorical Variables: [Explain how categorical variables were encoded] Feature Scaling/Normalization: [Specify if any scaling or normalization was applied] Exploratory Data Analysis: [Include any relevant visualizations Or insights gained from exploring the dataset] 5. Machine Learning Models Used: Model I : [Name Of the First Model] Justification: [Explain why this model was chosen] Model 2: [Name Of the Second Model] Justification: [Explain why this model was chosen] Model 3: [Name Of the Third Model] Justification: [Explain why this model was chosen] 6. Hyperparameter Tuning: Model I : [Specify Hyperparameters and Tuning Process] Model 2: [Specify Hyperparameters and Tuning Process] Model 3: [Specify Hyperparameters and Tuning Process] 7. Results: Performance Metrics: [Specify the evaluation metrics used, such as accuracy, precision, recall, Fl score, or relevant metrics for regression tasks] Model Comparison: [Present the results Of each model and compare their performance] Feature Selection Impact: [Discuss the impact Of feature selection on model performance, if applicable] Insights and Observations: [Provide insights gained from the analysis] If Classification is performed use this table MO del Mo del Mo del 2 Mo del 3 Accuracy Before Hyperpa rameter Tuni After Hyperpa ram eter Tunin Precision Before Hyperpa rameter Tuni After Hyperpa ram eter Tumn If Regression is performed use this table Mo del Mo del Mo del 2 Mo del 3 R2- Score Before Hyperpa rameter Tuni After Hyperpa rameter Tunin MSE Before Hyperpa rameter Tuni After Hyperpa ram eter Tunin Recall Before Hyperpa rameter Tuni Before Hyperpa rameter Tuni After Hyperpa rameter Turun After Hyperpa rameter Tunin Fl-Score Before Hyperpa rameter Tuni MPE Before Hyperpa rameter Tuni After Hyperpa ram eter Tunin After Hyperpa rameter Tunin 8. Conclusion: Summarize the key findings, lessons learned, and implications Of the project. Discuss any challenges faced and potential areas for future improvement,