The customer wanted to improve demand forecasting accuracy by implementing AI/ML based approach. Currently the forecasting is done in Excel with gut feeling than following any scientific approach. The current forecasting accuracy is around 60-70%
Python, Flask, Time Series Algorithms
The customer is a leader in water heater category in India. We have received product sales data at day level granularity for last 2 years. The data is tested to check missing values and outliers. The products were categorized based on the historical monthly sales data. Using feature engineering, the missing values were treated first. Using Dickey-Fuller test, the data was tested for Non Stationarity. The data did have trends, and seasonality, which was treated and the data was made stationary.
Later, various time series algorithms like ARIMA, Holt-Winters, XgBoost &LSTM were applied to the data., Holt-Winters gave an accuracy of over 90%, which was fine tuned by tuning hyper parameters. Other algorithms gave accuracy in the range of 75% to 85%
The customer’s customer ( An Organization of Govt. of India), wanted to do a Preventive Maintenance Proof of Concept for Air Conditioners. They asked to prepare a report on a set of data generated using various test applied on the Air Conditioners.
R, Data Visualization, K Means Clustering
The supplied data was provided only for 18 hours with details like Voltage, Current, Vibration, Compressor Temperature, Room Temperature, and date time component.
The data did not have a clearly defined breakdown condition. Neither the data was qualified for time series forecasting. It was clearly a case of un-supervised learning. We also for relationships between the variables and found that only Current, Vibration and Compressor temperature are good enough to represent the data set. We then applied K-Means Clustering algorithm to the data to create segments with data points which have more or less similar characteristics. With a K-Value of 15, we could distinctly identify on cluster which more or less, had data points, which were defining probable Air Conditioner breakdown condition.
A report on the findings was created with 3D visualizations of the Clusters along with our findings and submitted to the customer. We requested for more clearly defined breakdown conditions which can be used for more accurate predictions.
The customer is building a platform for automated buyer seller negotiation. The current version of the platform is relying on rule based engine. The customer wanted to use AI auto price discovery during buyer seller negotiation.
Python, Scikit-Learn, Flask, Tensorflow
Worked closely with the start up in North America to define how AI can be introduced in their current platform. Provided use cases relevant to the business domain and helped to create roadmap for AI implementation
Developed the model for buyer classification and seller classification to get intelligence about buyer & seller. Developed descriptive analytics solution for getting intelligence about previous deals to understand whether they were buyer centric or seller centric. Created API’s in Python flask to expose AI models as a rest web service. The next phase of the solution involved automating price discovery and buyer/seller negotiation.