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Entry-level Data Scientist with a passion for Data Analytics and Machine Learning.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Dedicated BBA undergraduate with a growing passion for Data Analytics and Machine Learning. Proficient in Python, Excel, and data visualization, with project experience covering data preprocessing, model development, performance tracking, and generating actionable insights. Eager to contribute analytical thinking and business understanding in an internship or entry-level data role.
Netaji subhas University
BBA
August 1, 2023 – June 30, 2026
Gyatri shiksha Niketan, Adityapur
Matriculation
N/A – May 31, 2020
Esteem Public school,Chaibasa
Intermediate
N/A – May 31, 2022
Customer Transaction Prediction
January 1, 2026 – June 1, 2026
Objective: Detect customer sentiment from feedback text. Algorithms Used: Logistic Regression. Python Libraries: NLTK, Scikit-Learn, Pandas, NumPy, Matplotlib, Seaborn. Key Steps: Tokenization, Stop-word removal, Lemmatization, TF-IDF transformation, Model training, Bias-Variance.
Customer Feedback Analysis
January 1, 2026 – June 1, 2026
Objective: Detect customer sentiment from feedback text. Algorithms Used: Logistic Regression. Python Libraries: NLTK, Scikit-Learn, Pandas, NumPy, Matplotlib, Seaborn. Key Steps: Tokenization, Stop-word removal, Lemmatization, TF-IDF transformation, Model training, Bias-Variance evaluation.
Bank customer Churn Prediction
January 1, 2026 – June 1, 2026
Objective: Predict whether a customer will leave or not. Algorithms Used: Random Forest Classification. Python Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn. Key Steps: Data preprocessing, Hyperparameter tuning, Train-Test Split, Performance evaluation using Classification Report & Accuracy Score. Outcome: Enabled accurate prediction of high-risk customers for retention planning.
Cultural Fit Analysis
The candidate's projects are all academic and focus on common data science problems (sentiment analysis, churn prediction). This indicates an interest in the field but provides limited insight into their ability to work in diverse team environments, adapt to different project methodologies, or handle real-world business constraints. The lack of professional experience or diverse project types limits the assessment of cultural fit beyond a basic alignment with data science interests.
Soft Skills & Operational Fit
The candidate lists 'Analytical thinking, Good Communication, Research skills' as soft skills. However, without direct assessment or work experience, it's difficult to validate their operational fit or the practical application of these skills in a professional setting. The project descriptions are clear but lack depth in problem-solving and decision-making processes.