🎯 AI and Data Engineer Intern at COS
🎓 MSc Data Science & Analytics | University of Westminster
📍 London, United Kingdom
📫 Connect with me: LinkedIn | 📧 saradabre1234@gmail.com
I’m a Data Scientist with a strong foundation in Mathematics, Statistics, and Applied Computing. I specialise in using data-driven approaches to solve business problems, uncover insights, and build predictive models that make an impact.
- 🔭 Currently exploring: Machine Learning, Deep Learning, and NLP applications
- 🌱 Passionate about: AI-driven decision-making and data storytelling
- 💬 Ask me about: Python, SQL, Machine Learning, or Data Visualisation
- ⚡ Fun fact: I see datasets as stories waiting to be told!
- Programming: Python, R, SQL
- Data Science: Machine Learning, Statistical Analysis, Predictive Modeling, NLP
- Libraries & Tools: Pandas, NumPy, Scikit-learn, TensorFlow, Matplotlib
- Visualization: Tableau, Power BI, Looker Studio, Google Data Studio
- Other: Data Wrangling, Feature Engineering, Model Optimization
- Soft Skills: Analytical Thinking, Problem Solving, Communication, Leadership
- Developed an end-to-end interactive Power BI dashboard tracking sales, profitability, and volume across the United States utilizing a relational Star Schema.
- Engineered advanced DAX calculations to support real-time dynamic metric toggling, automated contextual map headers, and custom text-handling layers to gracefully manage historical "No Data" states without breaking matrix totals.
- Integrated dynamic trend indicators (
▲/▼) using customUNICHARcodes to instantly telegraph Year-over-Year (YOY) percentage growth rates to stakeholders.
- Built ML models to predict app ratings using real Google Play Store data.
- Implemented Linear Regression, Random Forest, and KNN algorithms to analyze features such as installs, reviews, and categories.
- Performed data cleaning, feature scaling, and model evaluation using
$R^2$ and RMSE to assess accuracy.
- Developed time-series analysis and visualisations to identify stock performance trends.
- Used Python, Pandas, and Matplotlib to analyze closing prices, moving averages, and daily returns.
- Generated insights to help understand stock volatility and market movement patterns.
- Created regression models to predict Uber ride prices using feature engineering and hyperparameter tuning.
- Optimized model accuracy using cross-validation and evaluated performance metrics such as RMSE and MAE.
- Implemented ARIMA and SARIMAX models to forecast energy consumption trends.
- Evaluated time series models for predictive accuracy and trend consistency.
- Applied NLP techniques to analyze social media sentiment around Tesla’s Model 3.
- Performed text preprocessing, tokenisation, and sentiment scoring using Python NLP libraries.
MSc Data Science & Analytics
University of Westminster, London (2023–2024)
BSc Mathematics and Applied Computing
University of Mumbai (2018–2021)
- Data Analytics Essentials
- Tableau for Data Scientists
- Data Visualisation: Storytelling
- The Fundamentals of Digital Marketing
💼 LinkedIn
📧 saradabre1234@gmail.com
⭐ “Turning data into decisions — one model at a time.”