Objective:

Apply supervised learning techniques to a real$-$world dataset to solve a prediction problem. Use at least two different supervised learning algorithms to train models and perform a comparative analysis of their performance.

Dataset:

You may choose any real$-$world dataset of interest. Suggested sources include UCI Machine Learning Repository, Kaggle Datasets, or any other dataset relevant to your interests or field of study. Ensure the dataset involves a prediction task suitable for supervised learning $($either classification or regression$).$

Tasks:

Problem Statement: Clearly define the prediction problem you aim to solve with your chosen dataset.

Data Preprocessing:

Handle missing values, if any.

Perform necessary transformations $($e$.$g$.,$ encoding categorical variables, feature scaling$).$

Split the data into training and testing sets.

Model Training:

Apply at least two supervised learning algorithms $($e$.$g$.,$ Decision Trees, Linear Regression, SVM$,$ RandomForest, GradientBoosting, etc.$).$

For each model, tune relevant hyperparameters to optimize performance.

Model Evaluation:

Evaluate each model's performance using appropriate metrics $($e$.$g$.,$ accuracy, precision, recall, F$1$ score for classification; MSE, RMSE for regression$).$

Use cross$-$validation where appropriate.

Comparative Analysis:

Compare the performance of the models based on the evaluation metrics.

Discuss the strengths and weaknesses of each model in the context of the problem.

Deliverables:

A detailed report including:

Problem statement and dataset description.

Data preprocessing steps and rationale.

Detailed methodology for training and evaluating models.

Code snippets showcasing the key steps in preprocessing, model training, and evaluation.

Comparative analysis of the model performances.

Conclusions and possible directions for future work.

Code files used for analysis, preferably in a Jupyter notebook format.

Submission Guidelines:

Submit your report as a PDF document.

Include a link to your code files or Jupyter notebook $($e$.$g$.,$ a GitHub repository or a shared link to a Jupyter notebook$).$

Ensure your code is well$-$commented and organized to be easily understood.

Evaluation Criteria:

Clarity of Problem Statement: Clear and concise definition of the prediction problem.

Data Preprocessing: Effective handling and transformation of data for model training.

Methodology: Proper application and tuning of at least two supervised learning algorithms.

Model Evaluation: Comprehensive evaluation and correct application of evaluation metrics.

Comparative Analysis: Insightful comparison of model performances with supporting evidence.

Report Presentation: Overall organization, presentation of findings, use of visuals $($charts$,$ graphs$),$ and adherence to submission guidelines.

Getting Started Code Snippet:

# Example code snippet for loading data and basic preprocessing

import pandas as pd

from sklearn.model$\_$selection import train$\_$test$\_$split

from sklearn.preprocessing import StandardScaler

# Load dataset

data $=$ pd$.$read$\_$csv$(\text{'}$your$\_$dataset.csv$\text{'})$

# Basic preprocessing

# Assuming 'target' is the name of your target variable

X $=$ data.drop$(\text{'}$target$\text{'},$ axis$=1)$

y $=$ data$[\text{'}$target$\text{'}]$

# Splitting the dataset into training and testing sets

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}2,$ random$\_$state$=42)$

# Feature Scaling

scaler $=$ StandardScaler$()$

X$\_$train$\_$scaled $=$ scaler.fit$\_$transform$($X$\_$train$)$

X$\_$test$\_$scaled $=$ scaler.transform$($X$\_$test$)$

# Further steps would include model training, evaluation, and comparison as outlined in the tasks.

This code snippet is a starting point for data loading and preprocessing. It's important to adapt and extend it based on the specific requirements of your dataset and prediction task.