girishp92

Advanced-Real-Time-Self-Driving

Using Advanced Constitutional Neural Networks to make real autonomous driving possible in real time with human intuition

Classification-of-Iris-Data-using-Machine-learning-methods-in-Python

Human-activity-recognition-using-Recurrent-Neural-Nets-RNN-LSTM-and-Tensorflow-on-Smartphones

This was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep network. After the model was trained, it was saved and exported to an android application and the predictions were made using the model and the interface to speak out the results using text-to-speech API.

Meteors-impacted-earth-for-certain-period-visuzalized

This was course project done by me using some powerful tool such as d3.js

Missionares-and-Cannibals-Problem-Solver

Using the concepts of Artificial Intelligence. Solving the Missionares and Cannibals puzzle using DFS, BFS, Depth limited.

Supervised-learning-with-heterogenous-data-using-Random-Forest-algorithm

This was a group project where we are comparing the effectiveness of supervised learning using various multivariate data sets and i was involved doing so using Random Forest Model. I implemented the feature importance of various predictor variables and how it effects the error rate(RMSE). I used the Student Performance Dataset to show the importance of various predictor variables. I implemented it in Python using various libraries like Numpy, Scipy, Scikit-learn, pandas, matplotlib and seaborn packages for plotting the figures.

girishp92/Human-activity-recognition-using-Recurrent-Neural-Nets-RNN-LSTM-and-Tensorflow-on-Smartphones

This was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep network. After the model was trained, it was saved and exported to an android application and the predictions were made using the model and the interface to speak out the results using text-to-speech API.

girishp92/Supervised-learning-with-heterogenous-data-using-Random-Forest-algorithm

This was a group project where we are comparing the effectiveness of supervised learning using various multivariate data sets and i was involved doing so using Random Forest Model. I implemented the feature importance of various predictor variables and how it effects the error rate(RMSE). I used the Student Performance Dataset to show the importance of various predictor variables. I implemented it in Python using various libraries like Numpy, Scipy, Scikit-learn, pandas, matplotlib and seaborn packages for plotting the figures.

girishp92/Meteors-impacted-earth-for-certain-period-visuzalized

This was course project done by me using some powerful tool such as d3.js

girishp92/Missionares-and-Cannibals-Problem-Solver

Using the concepts of Artificial Intelligence. Solving the Missionares and Cannibals puzzle using DFS, BFS, Depth limited.

girishp92/Advanced-Real-Time-Self-Driving

Using Advanced Constitutional Neural Networks to make real autonomous driving possible in real time with human intuition

girishp92/Classification-of-Iris-Data-using-Machine-learning-methods-in-Python