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Project Title: Exploring Student Performance

Objective:

$-$ Load and clean a dataset containing student performance data

$-$ Perform data visualization and analysis to identify trends and correlations

$-$ Develop a predictive model to forecast student success

Dataset:

$-$ Use a sample dataset provided by the university or a public dataset $($e$.$g$.,$ UCI Machine Learning Repository$)$

Tasks:

$1.$ Data Preprocessing:

$-$ Load the dataset into a suitable data structure $($e$.$g$.,$ Pandas DataFrame$)$

$-$ Handle missing values and data cleaning

$-$ Normalize and transform data as needed

$2.$ Data Visualization:

$-$ Use libraries like Matplotlib and Seaborn to create visualizations $($e$.$g$.,$ histograms, scatter plots, bar charts$)$

$-$ Explore relationships between variables $($e$.$g$.,$ correlation matrix$)$

$3.$ Data Analysis:

$-$ Calculate summary statistics $($e$.$g$.,$ mean, median, standard deviation$)$

$-$ Perform hypothesis testing $($e$.$g$.,$ t$-$test, ANOVA$)$

$-$ Identify significant correlations and trends

$4.$ Predictive Modeling:

$-$ Split data into training and testing sets

$-$ Develop a predictive model $($e$.$g$.,$ linear regression, decision tree$)$

$-$ Evaluate model performance $($e$.$g$.,$ accuracy, precision, recall$)$

Deliverables:

$-$ A written report detailing the project's objectives, methods, results, and conclusions

$-$ A Python code script $($e$.$g$.,$ Jupyter Notebook$)$ implementing the tasks above

$-$ Visualizations and output from the analysis