Motivated entry-level Data Analyst with an MCA degree, ICT Academy certification, and hands-on experience in data analysis and machine learning projects. Skilled in Python, SQL, data cleaning, exploratory data analysis, …
Motivated entry-level Data Analyst with an MCA degree, ICT Academy certification, and hands-on experience in data analysis and machine learning projects. Skilled in Python, SQL, data cleaning, exploratory data analysis, and visualization. Strong analytical thinking, problem-solving ability, and proven experience explaining technical concepts clearly in academic environments.
Delivered computer science instruction
Evaluated performance
Guided foundational programming and digital skills development
Delivered computer science instruction, Evaluated performance, Guided foundational programming and digital skills development
Maintained institutional software systems, Supported databases, Implemented technical improvements
Maintained institutional software systems
Supported databases
Implemented technical improvements
Maintained institutional software systems, Supported databases, Implemented technical improvements
Data Science project to predict course success using ML Building a Machine Learning (ML) model to predict Course Success (or student performance) is a classic "Classification" problem. In the education industry, this is often called Early Warning Systems because it helps institutions identify students at risk of failing before the semester ends.Here is a professional breakdown of how you should describe this project in your portfolio or interviews.1. Project OverviewThe goal of this project is to predict whether a student will pass, fail, or drop out of a course based on historical data, demographic factors, and engagement levels.The Business ProblemHigh dropout rates affect university rankings and revenue. By predicting success early, academic advisors can provide targeted support (extra tutoring, counseling) to at-risk students.2. The Workflow (The ML Pipeline)Step 1: Data Collection & SourcesReal-world datasets for this project typically include:Demographics: Age, gender, region, and previous education level.Engagement Data (VLE): How many times did the student log in? How many clicks on study materials?Assessment Data: Scores from early-semester quizzes or mid-term assignments.Registration Data: How many days before the course start did the student register?Step 2: Data Preprocessing (Cleaning)Handling Missing Values: Using KNN Imputation or Mean/Median for missing quiz scores.Encoding Categorical Data: Converting "Gender" or "Education Level" into numbers using One-Hot Encoding.Scaling: Using StandardScaler to ensure that features like "Total Clicks" (thousands) and "Age" (tens) are on the same scale.Step 3: Feature Engineering (The "Secret Sauce")This is where the model gets its power. You create new columns like:Engagement Consistency: The standard deviation of clicks per week.Score Trend: Is the student’s score improving or declining over the first three assignments?Step 4: Model Selection & TrainingYou would typically compare multiple algorithms:Logistic Regression: A simple baseline model.Random Forest: Great for handling non-linear relationships in student data.XGBoost: Usually provides the highest accuracy for structured tabular data.Step 5: Model EvaluationSince the goal is to catch "failing" students, Accuracy is not enough. You focus on:Recall (Sensitivity): We want to ensure we catch all students who might fail, even if we accidentally flag a few who might pass.F1-Score: The balance between Precision and Recall.3. Industrial View: Tools & DeploymentIn a real-world remote job, you wouldn't just leave the code in a Jupyter Notebook. You would:Deploy as a Web App: Use Streamlit to create a dashboard where a teacher can input student details and get a "Success Probability."
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