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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

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.
Objective of the Project:
Problem Statement:
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]
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]
Machine Learning Models Used:
Model 1: [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]
Hyperparameter Tuning:
Model 1: [Specify Hyperparameters and Tuning Process]
Model 2: [Specify Hyperparameters and Tuning Process]
Model 3: [Specify Hyperparameters and Tuning Process]
Results:
Performance Metrics: [Specify the evaluation metrics used, such as accuracy, precision,
recall, F1 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
If Regression is performed use this table
Conclusion:
Summarize the key findings, lessons learned, and implications of the project. Discuss any
challenges faced and potential areas for future improvement.
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