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✉️ Email: ajinkyapatil229@gmail.com
📞 Phone: +91 9145474299

I’m a Data Scientist with a strong foundation in statistics, machine learning, and data analysis. My expertise spans developing recommendation systems, predictive models, and time series analyses that transform raw data into actionable insights. I am proficient in tools and languages such as Python, Pandas, Scikit-Learn, and TensorFlow, and have hands-on experience in full-cycle project development, from data preprocessing and modeling to deployment.

• M.Sc. in Statistics
  PES Modern College, Shivajinagar (Savitribai Phule Pune University)
  2021 – 2023 | Pune

• B.Sc. in Statistics
  PES Modern College, Ganeshkhind (Savitribai Phule Pune University)
  2018 – 2021 | Pune

• Higher Secondary Education
  Jawahar Navodaya Vidyalaya
  2016 – 2018 | Kolhapur

Objective: To analyze and predict cardamom price trends using ARIMA, VARMAX, and LSTM models.
Outcome: Developed accurate forecasting models to capture price fluctuations based on historical data and supply-demand dynamics.

Methodologies: Comparative analysis using Simple Linear Regression, Multiple Linear Regression, and Polynomial Fit.
Outcome: Evaluated and compared models based on metrics such as R-squared and MSE to determine the best approach for car price prediction.

Objective: Study the relationship between Gender Inequality Index (GII) and Human Development Index (HDI).
Outcome: Demonstrated a significant inverse relationship, highlighting the impact of gender inequality on development.

Methodology: Employed Multiple Linear Regression and Logistic Regression to analyze the effects of online education.
Outcome: Provided insights into both positive and negative impacts of online education on student performance.

• Statistics: Regression, Time Series Analysis, Probability, Hypothesis Testing
• Programming: Python (Pandas, NumPy, Scikit-Learn, TensorFlow, Matplotlib, Seaborn), SQL, R
• Data Visualization: PowerBI, Tableau, Matplotlib, Seaborn, Plotly
• Machine Learning: Supervised & Unsupervised Learning, Deep Learning, Recommendation Systems
• Data Analysis: Data Wrangling, Exploratory Analysis, Feature Engineering

• Machine Learning Optimization (National Programme on Technology Enhanced Learning)
• Introduction to Statistics (Stanford University, Coursera)
• Crash Course on Python (Coursera)
• SQL for Data Science (Coursera)
• Machine Learning with Python (IBM)
• Data Science Course (Internshala in association with NSDC)

I am always eager to connect with others passionate about data science and analytics. Feel free to reach out or explore my projects on GitHub.