Entry-Level Data Science Professional with strong analytical and machine learning skills
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Aspiring Data Analyst with a strong foundation in SQL, Python, Power BI, Excel, Statistics, and Data Visualization. Skilled in Data Cleaning, Exploratory Data Analysis (EDA), Machine Learning, and Business Intelligence through hands-on analytics projects. Experienced in transforming raw data into actionable insights using Pandas, NumPy, Scikit-learn and Power BI. Passionate about solving business problems through data-driven decision-making and analytical thinking.
ABES Engineering College, AKTU
Bachelor of Technology · Computer Science & Engineering
August 1, 2022 – June 30, 2026
1. Spam Detection System
January 1, 2026 – Present
Collected and preprocessed text datasets by removing stopwords, punctuation, and tokenization.Conducted EDA to identify patterns in spam and non-spam messages.Developed ML models (Naive Bayes, SVM, Logistic Regression) to classify messages as spam or non-spam.Evaluated using Accuracy, Precision, Recall, and Confusion Matrix.Achieved 94.2% classification accuracy in spam message detection.
2. Credit Wise Loan Approval System
January 1, 2026 – Present
Analyzed loan applicant datasets including income, credit score, employment and loan details.Performed data cleaning, preprocessing and feature selection.Produced EDA to identify factors impacting loan approval decisions.Built and evaluated classification models to predict loan approval (Approved / Rejected).Improved predictor performance through feature engineering and tuning.Achieved 91.3% model accuracy.
3. SmartCart Customer Segmentation System
January 1, 2026 – Present
Collected and analyzed customer demographics and purchase behavior data.Performed data cleaning, transformation and feature engineering.Conducted EDA to understand customer spending patterns and behavior.Applied K-Means clustering to segment customers into meaningful groups.Identified high-value customer segments for targeted marketing.Generated actionable insights to improve customer retention strategies.
Cultural Fit Analysis
The candidate's academic projects demonstrate a clear alignment with the target role of Data Science, focusing on practical applications of machine learning and data analysis. The diversity of projects (NLP, classification, clustering) indicates a broad interest within the field. However, without information on extracurricular activities, volunteer work, or professional experience, a comprehensive assessment of cultural fit is limited.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work through data analysis pipelines from data collection to insight generation. The academic nature of projects suggests a structured approach to problem-solving. However, without professional experience or psychometric test results, it's difficult to assess operational fit, stress handling, or team collaboration skills.