In this project, the client needed a model which predicts the transformer outages using Machine learning algorithm. Following are the steps which were taken:

• Researched on possible factors leading to transformer outages.

• Created MS Excel sheet with the variables, description, range and unit.

• Confirmed important variables with the client and created dummy dataset with the same.

• Implemented appropriate Machine Learning model on the dataset in R.

• Prepared documentation on the same.

NBA basketball statistics and and historical data was used to visualize the data. This data is displayed in two dashboards to give insights.

1. Dashboard with respect to teams

• Trend in the performance of the team

• Worst lost games and best win games of the season

• Against which team the team is strong and against which they are weak

• Performance varying when played in home ground and non-home grounds

2. Dashboard with respect to players

• Performance of players from game to game in a season

• Shooting success rate

• Against which teams they perform well and against whom they perform bad

HR analytics dataset was created in MS excel using formulae and then imported to MongoDB as per client request. The dataset was then accessed in python via MongoDB. Data cleaning were done on the required rows and the unnecessary columns were dropped. The Exploratory Data analysis and Feature Engineering was carried out to uncover the underlying structure of a data set To evaluate the model, the data was partitioned into train and test dataset. The model was generated using train dataset and tested on test dataset to generate new output values. These new values were verified with the actual outputs. The accuracies for prediction on both train case and test case were taken into consideration. For attrition rate prediction and performance analysis, random forest model was identified as best performing model and therefore used for prediction and deployment. Flask and HTML was used for creating a web app separately for attrition rate prediction as well as performance analysis.

NBA basketball statistics and historical data were used to visualize the data. This data is displayed in two dashboards to give insights. 1. Dashboard with respect to teams The trend in the performance of the team Worst lost games and best win games of the season Against which team the team is strong and against which they are weak Performance varying when played in home ground and non-home grounds 2. Dashboard with respect to players Performance of players from game to game in a season Shooting success rate Against which teams they perform well and against whom they perform badly

Remote Monitoring and Centralizing Genset: In this project, we monitored the various vital parameters of the Genset and it was turned off during extremities. We also plotted these parameters in real-time for the ease of operator and the required authorities were notified during extremities.