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.
<p>Delivered computer science instruction<br>
Evaluated performance<br>
Guided foundational programming and digital skills development</p>
Delivered computer science instruction, Evaluated performance, Guided foundational programming and digital skills development
Maintained institutional software systems, Supported databases, Implemented technical improvements
<p>Maintained institutional software systems<br>
Supported databases<br>
Implemented technical improvements</p>
Maintained institutional software systems, Supported databases, Implemented technical improvements
<p>Data Science project to predict course success using ML </p><p>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.</p><p>Here is a professional breakdown of how you should describe this project in your portfolio or interviews.</p>1. Project Overview<p>The 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.</p>The Business Problem<p>High dropout rates affect university rankings and revenue. By predicting success early, academic advisors can provide targeted support (extra tutoring, counseling) to at-risk students.</p>2. The Workflow (The ML Pipeline)Step 1: Data Collection & Sources<p>Real-world datasets for this project typically include:</p><ul><li>Demographics: Age, gender, region, and previous education level.</li><li>Engagement Data (VLE): How many times did the student log in? How many clicks on study materials?</li><li>Assessment Data: Scores from early-semester quizzes or mid-term assignments.</li><li>Registration Data: How many days before the course start did the student register?</li></ul>Step 2: Data Preprocessing (Cleaning)<ul><li>Handling Missing Values: Using KNN Imputation or Mean/Median for missing quiz scores.</li><li>Encoding Categorical Data: Converting "Gender" or "Education Level" into numbers using One-Hot Encoding.</li><li>Scaling: Using StandardScaler to ensure that features like "Total Clicks" (thousands) and "Age" (tens) are on the same scale.</li></ul>Step 3: Feature Engineering (The "Secret Sauce")<p>This is where the model gets its power. You create new columns like:</p><ul><li>Engagement Consistency: The standard deviation of clicks per week.</li><li>Score Trend: Is the student’s score improving or declining over the first three assignments?</li></ul>Step 4: Model Selection & Training<p>You would typically compare multiple algorithms:</p><ol><li>Logistic Regression: A simple baseline model.</li><li>Random Forest: Great for handling non-linear relationships in student data.</li><li>XGBoost: Usually provides the highest accuracy for structured tabular data.</li></ol>Step 5: Model Evaluation<p>Since the goal is to catch "failing" students, Accuracy is not enough. You focus on:</p><ul><li>Recall (Sensitivity): We want to ensure we catch <em>all</em> students who might fail, even if we accidentally flag a few who might pass.</li><li>F1-Score: The balance between Precision and Recall.</li></ul>3. Industrial View: Tools & Deployment<p>In a real-world remote job, you wouldn't just leave the code in a Jupyter Notebook. You would:</p><ul><li>Deploy as a Web App: Use Streamlit to create a dashboard where a teacher can input student details and get a "Success Probability."</li></ul><p></p>
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