Zainab Munir-| (- | Islamabad.
github.com/Zainab-1212
linkedin.com/zainab-munir
PROFESSIONAL SUMMARY
“Emerging AI/ML Engineer & Data Scientist | Skilled in Python, AI/ML/DL and Data
Analysis| Passionate about designing scalable AI solutions and data-driven insights to
solve real-world problems and drive business impact.”
SKILLS
❖ Programming Languages & Tools:
Python, TensorFlow, Keras, Scikit-learn, Jupyter, Google Colab, SQL, SPSS (basic), MS
Excel, MS Word, ChatGPT, Kaggle GPU.
❖ AI, ML & DL Expertise:
Data Analysis, Data Visualization, Supervised/Unsupervised Learning, Deep Learning,
Reinforcement Learning, K-Means, KNN, ANN, CNN, RNN, LSTM, XAI, RL, NLP (TF-IDF,
BoW, Lemmatization, Sentiment Analysis, Regex, NLTK), Image Processing, Time Series.
❖ Data & Visualization:
PCA, EDA, Pandas, NumPy, Matplotlib, Seaborn.
EXPERIENCE
v Machine Learning Intern (Virtual) | SkillifyZone, Swabi
(Jul ’25-Aug ‘25)
Worked individually & Build 4 ML projects in classification, clustering & predictive
modeling (Sales Trends Analysis, Student Performance, Customer Segmentation, Loan
Approval; End-to-End Mini ML Project) using Python, Scikit-learn & Streamlit; explored
tuning using GridSearchCV, joblib for saving best models & compare multiple models
using a leaderboard.
v Internee |Pakistan Bureau Of Statistics, Islamabad
(2021)
Worked in Sample Design & PSLM sections, handling sampling methodologies &
computing key variables using survey data. Collaborated with professionals & team
members to update MOUZA Census reports & perform data exercises - SPSS, MS Word.
ACHIEVEMENTS / CERTIFICATIONS
❖ Machine Learning & Artificial Intelligence – Hands-On Certification
(PNY-Rwp)
• Gained hands-on experience with core ML, AI and DL techniques: ANN, CNN, RNN,
LSTM, PCA, NLP, LLMs, XAI, and Reinforcement Learning using Python language.
• Solved 30+ real-world problems and built projects like Netflix Analysis,Fingerprint
Recognition, Reddit Sentiment Analysis, CNN on CIFAR-10, Time Series Forecasting,
Spam Detection (Naive Bayes), Clustering (K-Means), Decision Trees, and KNN-based
Iris Classification and more
• Main Tools & platforms: Scikit-learn, TensorFlow, Keras, NLTK, Google Colab, Jupyter
Notebook, Kaggle GPU, ChatGPT, Gemini, Claude AI.
• Solved Hacker Rank problems.
ACADEMIC PROJECTS
❖ Sales Trends Analysis of a Superstore (Data Understanding & Visualization)
• Learn to clean, explore, and visualize a real-world dataset. Cleaned & preprocessed
•
Superstore dataset (missing values, date parsing, data types) using Python, Pandas &
NumPy.
Performed EDA (grouping, filtering & aggregation ) to analyze sales across categories &
regions. and visualized sales insights with Matplotlib & Seaborn (bar charts, heatmaps,
line graphs) to present 3–5 key insights.
❖ Student Performance Predictor (Classification)
•
•
Predict if a student will pass or fail based on their features. Prepared student dataset
and defined pass/fail target variables; trained Logistic Regression & Decision Tree
models using Scikit-learn.
Evaluated results with accuracy, precision & confusion matrix, explaining feature impact
on outcomes using SHAP values.
❖ Customer Segmentation for a Retail Business
• Segment customers based on behavior and demographics through pattern recognition.
•
Scaled features (Annual Income, Spending Score, Age) with StandardScaler and applied
K-Means clustering; evaluate clusters with Elbow method.
Visualized clusters in 2D using PCA (dimensionality reduction) and scatter plots, drawing
insights.
❖ Loan Approval Prediction System (End-to-End mini ML Pipeline)
• Build a complete ML solution from data cleaning to prediction. Preprocessed dataset
•
•
(null handling, categorical encoding) and trained multiple classification models with
Scikit-learn.
Saved best model using joblib and deployed an interactive Streamlit interface for realtime loan approval prediction.
Enhanced pipeline with GridSearchCV tuning, use leaderboard for model comparison.
❖ Netflix- Performed; EDA and Trend Analysis
• Performed EDA on Netflix dataset to analyze content types, genres and release patterns.
• Explored audience preferences, ratings, and popular shows across countries.
• Utilized Python libraries (Pandas, Matplotlib, Seaborn) for data cleaning and
visualization.
❖ Emotion Detection with NLP & ML
• Preprocessed text by cleaning & lemmatizing messages; extracted features using TF-IDF.
• Built and evaluated multiple ML models, including neural networks using Keras with
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TensorFlow backend, SVM, Random Forest, and others for emotion classification.
Visualized results & leveraged Kaggle GPU for model training efficiency.
❖ Global Terrorism Data Analysis
• Analyzed Global Terrorism dataset (180,000+ attacks) to identify top attack types,
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weapon impact, and most affected countries and nationalities -).
Conducted time-series and safety analysis using EDA and visualizations like bar graphs
and pie charts to reveal trends and insights.
❖ Zomato Data Analysis
• Analyzed customer behavior, restaurant ratings, and online/offline order patterns using
•
the Zomato dataset.
Conducted EDA on cuisines, pricing, and location-based demand; visualized insights with
Pandas, NumPy, Matplotlib, Seaborn, and heatmaps to support business improvement.
EDUCATION
• Bachelors in Statistics | Quaid-i-Azam University
