Hello I'm
Nischitha D
Data Analysis and Data Science
About Me
I am a certified Data Scientist and Data Analyst from IIT Bombay with a proven track record of successfully completing 10+ industry projects. These projects encompass a diverse range, from 'Ethereum Price Prediction' to 'Statistical Analysis on Big Mac Index Case Study,' 'COVID-19 Detection using lung CT Scan and deploying it through a Flask web app,' and 'Breast Cancer Prediction.'
My proficiency extends beyond project work, as evidenced by my five-star rating as a Python coder on HackerRank, attesting to my strong coding abilities and problem-solving acumen. Additionally, I possess five months of valuable communication experience, showcasing my strong persuasion skills. This experience has equipped me with the ability to effectively handle clients and build lasting relationships.
Education
Don Bosco Institute of Technology
B.E , Computer Science Engineering
Present
CERTIFICATES
Skills
Python ⭐⭐⭐⭐
SQL ⭐⭐⭐⭐
Data Analytics ⭐⭐⭐⭐
Pandas ⭐⭐⭐⭐
Power BI ⭐⭐⭐⭐
Flask ⭐⭐⭐
Exploratory Data Analysis ⭐⭐⭐⭐
Microsoft Excel ⭐⭐⭐⭐
Statistics Analysis ⭐⭐⭐⭐
Regression Modeling ⭐⭐⭐⭐
Machine Learning ⭐⭐⭐⭐
Predictive Modeling ⭐⭐⭐⭐
Communication ⭐⭐⭐⭐⭐
Sales ⭐⭐⭐⭐
Entraprenurship ⭐⭐⭐⭐
Public Speaking ⭐⭐⭐⭐
Internships Experience
Data Science and Business Analyst Intern
Certificate
September 2023 - October 2023
Internship
Data Science Intern
Augest 2023 - Augest 2023
Internship
Business Development Associate
Certificate
Augest 2022 - December 2022
Internship
Workshop
In the workshop, I mastered AI integration in Power BI, transforming my work into a captivating and 10x more efficient data storytelling experience.
Projects
ML Facial recognition to detect mood and suggest songs accordingly
Develop a CNN-based facial recognition model with six mood classes (happy, fear, sad, disgust, angry, surprise) using a labeled dataset. Upon detecting the mood, integrate the model with a music recommendation system to suggest songs that match the detected emotional state, providing users with an immersive music experience.
Next Word Prediction
Create a Recurrent Neural Network (RNN) using TensorFlow and Keras, preprocessing text data into sequences with target labels as the next word. Train the RNN on a large text corpus, optimizing it to predict the most probable next word given a sequence of words. This model can be used for various natural language processing tasks like auto-completion or text generation.
Develop A Neural Network That Can Read Handwriting
Create a neural network machine learning project for MNIST Handwritten Digit Classification using TensorFlow and Convolutional Neural Networks (CNNs). This project aims to accurately recognize and classify handwritten digits from 0 to 9, serving as a fundamental example of image classification in deep learning. The CNN model is designed to learn intricate features from the digit images to achieve high accuracy in classification tasks.
Indian Premier League (IPL) Exploratory Data Analysis
Conducted comprehensive EDA on IPL data, illuminating the league's triumphs. Unveiled the most successful teams, star players, and the intricate factors influencing team victories and losses. Delivered actionable insights that redefine the game.
Exploratory Data Analysis on terrorism
comprehensive exploration of the Terrorism Dataset to uncover hot zones and gain insights into the patterns and trends of terrorist activities. By delving into this EDA
Car Price Prediction
In Car Price Prediction I utilized the train_test_split function from the Scikit-learn (sklearn) library to split the dataset into training and testing subsets, enabling effective model evaluation and validation. I used the decision tree regression algorithm to train a car price prediction model , achieving accuraccy about 91.75%.
Ethereum-Future-Price-Prediction
Utilized NumPy, Pandas, and sci-kit-learn, employing LSTM neural networks to forecast future Ethereum (ETH) prices based on historical data.
Breast Cancer Prediction
Employed Python's Pandas, NumPy, and Scikit-learn, I delved into breast cancer data, using Logistic Regression, Decision Trees, and Random Forest models to achieve precise diagnoses with high accuracy, revolutionizing disease detection through advanced machine learning
Covid-19 Detection Using Lungs CT Scan
I created a robust COVID-19 detection system using a Convolutional Neural Network (CNN) with Keras and TensorFlow, achieving a 93% validation accuracy in 10 epochs and deploying it seamlessly through a Flask web app.
Bike Share Demand
I analyze bike-sharing data and create an XGBoost regression model with optimized parameters using GridSearchCV, covering preprocessing, visualization, and feature engineering. The final model's robustness is assessed through cross-validation, evaluating metrics like Mean Squared Error and R-squared.
