Real World Data Science Projects and Case Studies


    The main idea of this Data Science project is to develop a real-time machine learning model that can correctly detect and predict the future events. It will improve the efficiency and optimizing the production processes, businesses are able to increase the productivity.

Data may be numbers, text, image, video or audio. Text analysis using a NLP algorithm and Named Entity Recognition. predictice analysis, diagnostic analytics implemented using regression or classification models. Deep learning model RNN or LSTM are used for image or video analytics.

Industry Expertise

  • 1Financial Analytics
  • 2 Marketing and stretgic Analysis
  • 3Big data and Analytics in the automotive industry
  • 4Health Care Analysis
  • 5Data analytics solutions transformed the farming industry
  • 6Customer Segmentation and Product Recommendation
  • 7Life science and Insurence Industry
  • 8Big Data Analytics in Manufacturing Industry

List of Data science Projects

  • 1Identify trends in market segments
  • 2 Prevent customer Churn
  • 3Fraud Detection Through Claims Investigation
  • 4Social Media Analytics
  • 5Car Fuel Consumption
  • 6Used car price prediction
  • 7Traffic Data Analysis
  • 8Anomaly detection
  • 9Cybercrime Prevention
  • 10Supply Chain Analytics
  • 11Content Enrichment
  • 12Text analytics - NLP
  • 13Automated Number Plate Recognition (ANPR)
  • 14Industrial Safety Analysis
  • 15Semantic Indexing
  • 16Value-driven data Analytics
  • 17Agricultural Price Forecasting Using Neural Network Model
  • 18Product profitability Analytics
  • 19Conversational AI - Chatbot
  • 20Image and Video Analytics
  • 19Fire detection
  • 20Sentiment Analysis
  • 21Traffic Signs Recognition
  • 22Climate Change Impacts on the Global Food Supply
  • 23Fake News Detection
  • 24Air Pollution Prediction
  • 25Age and Gender Detection
  • 26Time Series Modelling
  • 27Taxi Trip Time Prediction
  • 28Job Recommendation System


Data science Workflow

  • Collect data. Use your digital infrastructure and other sources to gather as many useful records as possible and unite them into a dataset.
  • Prepare Data. Prepare your data to be processed in the best possible way. Data preprocessing and cleaning procedures can be quite sophisticated, but usually, they aim at filling the missing values and correcting other flaws in data, like different representations of the same values in a column (e.g. December 14, 2016 and 12.14.2016 won’t be treated the same by the algorithm).
  • Split data. Separate subsets of data to train a model and further evaluate how it performs against new data.
  • Train a model. Use a subset of historic data to let the algorithm recognize the patterns in it.
  • Test and validate a model. Evaluate the performance of a model using testing and validation subsets of historic data and understand how accurate the prediction is.
  • Deploy a model. Embed the tested model into your decision-making framework as a part of an analytics solution or let users leverage its capabilities (e.g. better target your product recommendations).
  • Iterate. Collect new data after using the model to incrementally improve it.

Projects setup

    Once you are done learning theoretical concepts, you should start working on AI and machine learning projects. These projects will give you the practice necessary to hone your skills in the field, and at the same time, are a great value add to your machine learning portfolio. Implementation will be R program, Python, Big Data, Hadoop, PySpark and No-SQL databases.

Our Pallikaranai, Data science development centre offers client project in Machine learning, Deep learning, data analytics and AI.

Detailed list of projects..