Instructions for the recorded video$$ Create a recorded video $($screencast or presentation format$)$ where team membersexplain their project.$$ Cover key aspects of the project, including problem statement, dataset characteristics,data preprocessing, model selection rationale, feature selection, hyperparameter tuning,and results analysis.$$ The video should be well$-$structured, clear, and provide a comprehensive overview ofthe entire project.

Project ReportTeam Members with Student NumberProject Title$1.$ Objective of the Project:$2.$ Problem Statement:$3.$ Dataset Details:$$ Dataset Name: $[$Name of the Dataset$]$ Source: $[$Provide the source or origin of the dataset$]$ Size: $[$Number of instances, features, and target variable$]$ Description: $[$Brief overview of the dataset, including the nature of features and thetarget variable$]4.$ Data Preprocessing:$$ 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 gainedfrom exploring the dataset$]5.$ Machine Learning Models Used:$$ Model $1$: $[$Name of the First Model$]$Justification: $[$Explain why this model was chosen$]$ Model $2$: $[$Name of the Second Model$]$Justification: $[$Explain why this model was chosen$]$ Model $3$: $[$Name of the Third Model$]$Justification: $[$Explain why this model was chosen$]6.$ Hyperparameter Tuning:$$ Model $1$: $[$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, F$1$ 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, ifapplicable$]$Insights and Observations: $[$Provide insights gained from the analysis$]$If Classification is performed use this tableModelNameAccuracy Precision Recall F$1-$ScoreModel$1$BeforeHyperparameterTuningAfterHyperparameterTuningBeforeHyperparameterTuningAfterHyperparameterTuningBeforeHyperparameterTuningAfterHyperparameterTuningBeforeHyperparameterTuningAfterHyperparameterTuningModel$2$Model$3$If Regression is performed use this tableModelNameR$2-$ Score MSE MAE MPEModel$1$BeforeHyperparameterTuningAfterHyperparameterTuningBeforeHyperparameterTuningAfterHyperparameterTuningBeforeHyperparameterTuningAfterHyperparameterTuningBeforeHyperparameterTuningAfterHyperparameterTuningModel$2$Model$38.$ Conclusion:Summarize the key findings, lessons learned, and implications of the project. Discuss anychallenges faced and potential areas for future improvement