Projects
Explore the skilful craftsmanship and meticulous attention to detail that characterize our featured project.
Air Pollution Analysis using Time Series Techniques
Python || Pandas || NumPy || Seaborn || Matplotlib || pmdarima (Auto ARIMA)
Forecasting urban air pollution levels using time series modeling of historical PM2.5 data
Building predictive tools for environmental monitoring, public health safety, and smart urban planning
Course Chapters
Time Series Preprocessing and Resampling
Python Libraries for Pollution Data Analysis
Data Cleaning and Interpolation for Missing Values
Exploratory Visualization with Seasonal Trend Analysis
Monthly Forecasting Using Auto ARIMA (pmdarima)
Forecast Visualization with Confidence Intervals
Model Evaluation using MAE & RMSE
Real-world Applications in Policy and Health
Certificate of Completion
Named Entity Recognition using spaCy v3
Python || spaCy || NLP || BIO Tagging || DocBin || Transformers || Data Annotation
Automating entity extraction from unstructured text using a custom-trained NER model built with spaCy v3
Transforming raw text into structured data to power intelligent applications in document processing and information retrieval
Course Chapters
Data Preprocessing and BIO Tag Conversion
Token Grouping and Label Structuring for NER
Exploratory Analysis of Entity Distribution
Visualization with spaCy's displaCy Tool
Training Custom NER Pipeline using spaCy v3
Model Architecture Configuration (Transformer / CNN)
Evaluation using Precision, Recall, and F1-Score
Deployment for Real-World NLP Use Cases
Certificate of Completion
Predictive Car Insurance Claim Model using XGBoost & CatBoost
Python || Classification || Regression || XGBoost || CatBoost || Random Forest || Risk Modeling
Building a dual-model pipeline to classify claim likelihood and predict claim costs using policyholder data
Transforming insurance operations through machine learning for proactive risk assessment and liability forecasting
Course Chapters
Data Loading, Cleaning, and Feature Engineering
Encoding Categorical Variables (OneHot, Ordinal)
Outlier Detection and Log Transformation
Exploratory Data Visualization (Heatmaps, Boxplots, Countplots)
Classification Models: Logistic Regression, Random Forest, XGBoost, CatBoost
Regression Models: Linear Regression, Decision Trees, Random Forest
Hyperparameter Tuning with GridSearchCV
Evaluation Metrics: Accuracy, F1, ROC-AUC, MAE, RMSE, R²
Deployment Insights for Insurance Underwriting and Claim Management
Certificate of Completion
Global Temperature Change Prediction using Time Series Techniques
Python || Pandas || NumPy || Seaborn || Matplotlib || pmdarima (Auto ARIMA)
Forecasting climate change trends through advanced time series modeling of global temperature anomalies
Uncovering long-term warming patterns, validating climate narratives, and enabling data-driven environmental insights
Course Chapters
Data Handling and Time Series Preprocessing
Python Libraries for Climate Data Analysis
Exploratory Data Analysis using Seaborn and Matplotlib
Time Series Modeling with Auto ARIMA (pmdarima)
Forecasting Future Temperature Trends
Performance Evaluation using MAE & RMSE
Interpreting Climate Change through Predictive Analytics
Certificate of Completion
Netflix Recommender System
ML || Python || Pandas || NumPy || Scikit-learn || Matplotlib || Seaborn
Building a Movie Recommendation System using TMDB 5000 Movie Dataset
Exploring Demographic Filtering
Content Based Filtering, and Collaborative Filtering to enhance user experience and engagement in the age of data-driven recommendations.
Course Chapters
What is Data Preprocessing?
What is Exploratory Data Analysis (EDA)?
Various types of filtering techniques
What is Demographic Filtering?
What is Content-Based Filtering?
What is Collaborative Filtering?
Learn about Text Vectorization and Matrix Factorization
How to evaluate the performance of Machine Learning Algorithms?
Evaluation Metrics for Recommendation Systems
Tuning and Optimization of Machine Learning Algorithms
Certificate of Completion
Starbucks Customer Segmentation
Python || Pandas || NumPy || Scikit-learn || Matplotlib
Enhancing Starbucks marketing with Customer Segmentation
Leveraging data analytics and machine learning to identify and target distinct customer segments
Fostering personalized campaigns for increased loyalty and targeted sales growth
Course Chapters
Exploratory Data Analysis (EDA) and Data Preprocessing on Customer Dataset
Feature Selection for Customer Segmentation
Python Programming for Data Analysis
Pandas and NumPy for Data Manipulation
Visualizing Customer Segments with Matplotlib
Unsupervised Machine Learning for Segmentation
Implementing Clustering Algorithms with Scikit-learn
Personalized Campaign Strategies
Increasing Customer Loyalty through Data Analytics
Targeted Sales Growth with Machine Learning
Certificate of Completion
Uber Data Analysis
Python || Pandas || NumPy || Scikit-learn || Matplotlib
Unveiling business insights through in-depth Uber Data Analysis for New York
Estimating annual revenue, tracking growth, and informing strategic decisions for enhanced business performance
Course Chapters
Data Exploration and preprocessing
Python Libraries for Data Analysis
Data Manipulation using NumPy and Pandas
Data Visualization using Matplotlib and Seaborn
How to analyze Annual Revenue and track Growth Trends
Extracting Business Insights and drafting Strategies
Enhance Business Performance through Data Analysis
Certificate of Completion
Loan Application Predictor
Python || Pandas || NumPy || Scikit-learn
Developing a Loan Application Predictor using machine learning to analyze key financial details and predict the likelihood of loan approval
Aiding lenders in informed decision-making
Course Chapters
Data Preprocessing for Loan Application Data
Feature Engineering and Selection
Building a Machine Learning Model for Prediction
Using Scikit-learn for Model Development
Python Programming for Machine Learning
Pandas and NumPy for Data Manipulation
Evaluating Model Performance
Supporting Informed Decision-Making for Lenders
Certificate of Completion
Hotel Booking DA and Cancellation Predictions
Python || Pandas || NumPy || Scikit-learn || Matplotlib || Seaborn
Empowering hotel managers through Data Analysis and Cancellation Prediction
Optimizing booking strategies
Minimizing revenue loss for enhanced operational efficiency
Course Chapters
Exploratory Data Analysis (EDA) on Hotel Data
Data preprocessing using Python
Feature Engineering for Cancellation Prediction
Python Programming for Data Analysis
Pandas and NumPy for Data Manipulation
Visualizing Data Patterns with Matplotlib and Seaborn
Developing a Machine Learning Model for Prediction
Utilizing Scikit-learn for Model Implementation
Optimization of Booking Strategies
Minimizing Revenue Loss through Predictive Analysis
Certificate of Completion
IMDB Movie Sentiment Analysis
Python || Spyder IDE || NLTK || Scikit-learn || Spacy || Matplotlib || Seaborn || NLP || Gensim
Developing an IMDB Movie Sentiment Analysis model
Utilizing Python, NLP, and machine learning techniques for accurate classification of movie reviews as positive or negative
Facilitating quick insights into sentiment for informed viewing decisions
Course Chapters
Exploratory Data Analysis (EDA) and Data Preprocessing on Movie Review Dataset
Text Preprocessing for NLP
Utilizing NLP Libraries (NLTK, Spacy, Gensim) for Text Analysis
Feature Extraction from Text Data
Supervised Machine Learning for Sentiment Analysis
Implementing Classification Models with Scikit-Learn
Deep Learning for Sentiment Analysis with Keras and TensorFlow
Model Evaluation and Fine-Tuning
Integration of NLP and Machine Learning in Sentiment Analysis
Leveraging IMDB Movie Review Dataset for Model Training
Quick Insights into Sentiment for Informed Viewing Decisions
Certificate of Completion
Covid-19 Cases Prediction with Python
Python || Pandas || Statsmodels || Scikit-learn || Matplotlib || Seaborn || Tensorflow/Keras
Performing time series forecasting for COVID-19 cases using Python
The project involves data cleaning, exploratory data analysis, and the application of various time series forecasting models such as ARIMA, SARIMA, Prophet, and LSTM.
The outcome will aid in predicting future COVID-19 trends, facilitating informed decision-making for health officials and policymakers
Course Chapters
Data Preprocessing and Exploratory Data Analysis on COVID-19 Time Series Data
What is ARIMA (AutoRegressive Integrated Moving Average)?
What is SARIMA (Seasonal ARIMA)?
What is the Prophet procedure?
What is LSTM (Long Short-Term Memory) - A type of recurrent neural network
Implementation of ARIMA, SARIMA, Prophet, and LSTM models in Python and Evaluation Metrics
Time Series cross-validation and Hyperparameter Tuning
Utilizing Python libraries for Time Series Analysis (e.g., Pandas, NumPy, Matplotlib, Statsmodels, Tensorflow)
Certificate of Completion
Credit Card Fraud Detection
Python || Pandas || NumPy || Scikit-learn || TensorFlow/Keras
Building a Credit Card Fraud Detection model
Employing machine learning algorithms for accurate identification of fraudulent transactions
Emphasizing high precision, recall, and accuracy to enhance financial security and minimize false positives
Course Chapters
Exploratory Data Analysis (EDA) and Data Preprocessing on Credit Card Transaction Data
Handling Class Imbalance in Fraud Detection using Techniques like SMOTE
What is Supervised Machine Learning?
Machine Learning Algorithms such as Logistic Regression, Decision Trees, Random Forests
Support Vector Machines (SVM) and Gradient Boosting
Evaluation Metrics for Classification Algorithms such as Precision, Recall, Confusion Matrix, ROC curve
Hyper Parameter Tuning for Improved Model Performance
Utilization of Scikit Learn for implementation
Certificate of Completion
Heart Disease Prediction
Python || Pandas || NumPy || Scikit-learn || TensorFlow/Keras
Utilizing machine learning algorithms to predict heart disease based on patient features.
Deliverables include a documented model and a user-friendly interface for early detection and prevention in healthcare.
Course Chapters
Exploratory Data Analysis (EDA) and Feature Engineering on the dataset
Python Libraries such as NumPy and Pandas for Data Manipulation
Data Visualization using Matplotlib and Seaborn
What is Supervised Machine Learning?
Using Scikit Learn to implement algorithms such as Logistic Regression,
Decision Trees, Random Forests, Support Vector Machines (SVM), k-nearest Neighbors
and Gradient Boosting
What are Model Evaluation Metrics?
Hyperparameter Tuning for Improved Model Performance
Creating a User-Friendly Interface for Early Detection
Model Deployment for Healthcare Applications
Certificate of Completion
Online Food Order Prediction
Python || Pandas || NumPy || Scikit-learn
Enhancing Food Delivery
Implementing a machine learning-based Online Food Order Prediction system to optimize operations
Improve delivery efficiency by predicting customer behavior and demand patterns
Course Chapters
Exploratory Data Analysis (EDA) and Feature Engineering
Time Series Analysis for Order Demand Patterns
What is Supervised Machine Learning?
Machine Learning Algorithms such as Logistic Regression, Decision
Trees, Random Forests, Support Vector Machines and Gradient Boosting
Evaluation Metrics for Performance Evaluation of Predictive Models
Hyperparameter Tuning for Improved Model Performance
Implementation of Machine Learning Algorithms using Scikit Learn
Real-Time Implementation and Integration into Food Delivery Systems
Operational Optimization for Improved Delivery Efficiency
Certificate of Completion
Big Mart Sales Prediction
Python || Pandas || NumPy || Scikit-learn || Matplotlib || Seaborn
Optimizing Retail Strategy
Developing a Big Mart Sales Prediction model to forecast product sales
Leveraging data analysis and machine learning for accurate predictions and strategic inventory optimization
Course Chapters
Exploratory Data Analysis (EDA) and Feature Engineering on the dataset
Time Series Analysis for Forecasting Sales Trends
What is Supervised Machine Learning?
Machine Learning Algorithms such as Linear Regression, Decision Trees, Random Forests,
Support Vector Machines (SVM) and Gradient Boosting
Implementation of Machine Learning Algorithms using Scikit Learn
What are Machine Learning Model Evaluation Metrics?
Hyperparameter Tuning for Improved Model Performance
Forecasting Product Sales using Machine Learning
Strategic Inventory Optimization based on Predictions
Real-time Implementation and Integration into Retail Systems
Certificate of Completion
Employee SQL-Tableau Integration
SQL || Tableau
Revolutionizing Workforce Management
Integrating SQL employee data into Tableau for real-time, interactive dashboards
Empowering informed decisions on performance, compensation, and demographics for enhanced productivity
Course Chapters
SQL Database Integration
Data Cleaning and Preparation
Tableau Dashboard Development
Performance Metrics and KPIs
Compensation Analysis
Demographic Insights
Real-Time Data Updates
User Empowerment and Decision-Making
Productivity Analysis
Collaboration between SQL and Tableau
Business Intelligence Best Practices
Certificate of Completion
Employee Attrition Prediction
Python || Jupyter Notebook || Pandas || NumPy || Scikit-learn || Matplotlib || Seaborn
Employee Retention Empowered
Building a Python-based machine learning model to predict and mitigate attrition
Leveraging data-driven insights for proactive employee management
Course Chapters
Exploratory Data Analysis (EDA) and Feature Engineering on Employee dataset
Understanding Class Imbalance in Attrition Prediction
What is Supervised Machine Learning?
Machine Learning Algorithms such as Logistic Regression, Decision Tress, Random Forests,
Support Vector Machines (SVM) and Gradient Boosting
Implementation of Machine Learning Algorithms using Scikit Learn Library
What are Evaluation Metrics for Model Performance?
Hyperparameter Tuning for Improved Model Performance
Employee Attrition Prediction Using Machine Learning and Real-Time Implementation
Leverage Data-driven Insights for Employee Retention
Certificate of Completion
Breast Cancer Prediction
Python || Spyder IDE || NLTK || Scikit-learn || Spacy || Matplotlib || Seaborn || NLP || Gensim
Empowering Early Diagnosis
Developing a machine learning model for Breast Cancer Prediction using diverse algorithms
Ensuring accurate classification of breast lumps as malignant or benign to enhance healthcare outcomes.
Course Chapters
Exploratory Data Analysis (EDA) and Feature Engineering on the dataset
Understanding Class Imbalance in the Cancer Data
What is Supervised Machine Learning?
Machine Learning Algorithms such as Logistic Regression, Decision Tress, Random Forests,
Support Vector Machines (SVM), K-Nearest Neighbors and Neural Networks
Implementation of Machine Learning Algorithms using Scikit Learn library
What are Evaluation Metrics for Model Performance?
Hyperparameter Tuning for Improved Model Performance
Breast Cancer Prediction Using Machine Learning
Leveraging DIverse Algorithms for Robust Predictions
Certificate of Completion
Stock Price Prediction
Python || Jupyter Notebook || Pandas || NumPy || Scikit-learn || Matplotlib || Seaborn || TensorFlow/Keras
Forecasting Financial Markets
Developing a Stock Price Prediction model using machine learning to analyze historical data
Enhancing investors decision-making with accurate predictions of future stock price trends
Course Chapters
Time Series Analysis of Historical Stock Data
What is Feature Selection and Feature Engineering?
What is Supervised Machine Learning?
Machine Learning Algorithms such as Linear Regression, Decision Trees,
Random Forest, Support Vector Machine (SVM), Time Series Models(ARIMA, SARIMA),
Long Short-Term Memory (LSTM) Networks
Implementation of Machine Learning Algorithms using Scikit Learn Library
What are Evaluation Metrics for Machine Learning Models?
Hyperparameter Tuning for Improved Model Performance
Implementing Ensemble Techniques for Robust Predictions
Certificate of Completion
Exploratory Data Analysis on Terrorism
Python || Jupyter Notebook || Pandas || NumPy || Scikit-learn || Matplotlib || Seaborn || SciPy || Statsmodels
Exploratory Data Analysis on global terrorism data using Python
Utilizing statistical analysis, machine learning models, and visualization to uncover patterns and predict incidents
Aiming to inform decision-making with valuable insights into the factors driving terrorism.
Course Chapters
Exploratory Data Analysis (EDA) and Feature Engineering on Terrorism dataset
Classification Models and Clustering Analysis
Data Manipulation using Python libraries such as NumPy and Pandas
Utilization of Matplotlib and Seaborn for Data Visualization
Incorporating Geographic and Temporal Factors for Analysis
Decision Support through Informed Data Analysis
Certificate of Completion
Exploratory Data Analysis on Covid - 19
Python || Jupyter Notebook || Pandas || NumPy || Scikit-learn || Matplotlib || Seaborn || SciPy || Statsmodels
Analyzing COVID-19 Trends
Conducting Exploratory Data Analysis using Python to glean insights from diverse sources
Employing statistical analysis, machine learning models, and visualization techniques to predict and understand the impact of the pandemic, aiding informed decision-making
Course Chapters
Exploratory Data Analysis (EDA) and Feature Engineering on the COVID-19 dataset
Statistical Analysis to Understand COVID-19 Trends
What are Time Series Models such as ARIMA, SARIMA, etc
Data Manipulation using Python Libraries such as NumPy and Pandas
Data Visualization using Python Libraries such as Matplotlib and Seaborn
Interpretation of COVID-19 Insights through Visualization
Incorporating External Factors (e.g., Mobility Data, Weather) for Analysis
Real-time Data Updates and Monitoring
Decision Support through Informed Data Analysis
Certificate of Completion
Data Analysis on Employee Data using Tableau-SQL Integration
SQL || Tableau
Integrate SQL employee data with Tableau
Providing real-time insights into employee performance, compensation, and demographics for informed decision-making
Course Chapters
SQL Database Connection
Data Extraction
Data Cleaning and Preprocessing
Tableau Dashboard Design
Performance Metrics and KPIs
Data Integration
Real-Time Data Updates
User-Friendly Interface
Informed Decision-Making
Ethical Considerations
Security Measures
Documentation
Certificate of Completion
Pix2Pix Image to Image Translation With a Conditional GAN
TensorFlow (or PyTorch) for deep learning implementation || Python programming language
Pix2Pix employing a conditional generative adversarial network
Transforms input images into corresponding outputs
Enabling diverse applications in image-to-image translation without explicit paired training data
Course Chapters
What are Generative Adversarial Networks (cGAN)?
What is Pix2Pix Architecture?
Understanding Image-to-Image Translation
Exposure to Tensorflow, PyTorch, OpenCV, Pix2Pix Implementation
Knowledge about GPU for accelerated model training (optional but recommended)
Certificate of Completion