Pinned Repositories
boston_housing
The Boston housing market is highly competitive, and you want to be the best real estate agent in the area. To compete with your peers, you decide to leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home. Luckily, you’ve come across the Boston Housing dataset which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. Your task is to build an optimal model based on a statistical analysis with the tools available. This model will then be used to estimate the best selling price for your clients' homes.
Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
finding_donors
In this project, I apply supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause. I first explored the data to learn how the census data is recorded. Next, I applied a series of transformations and preprocessing techniques to manipulate the data into a workable format. I then evaluated several supervised learners, and considered the best suited for the solution. Then, I optimized the model and finally, explored the chosen model and its predictions under the hood, to see just how well it's performing when considering the data it's given.
titanic_survival_exploration
This is the first project (p0) of Udacity's machine learning nanodegree. It is implementation of the decision tree algorithm to accurately predict survival of passengers on the titanic ship.
sneha-nishtala's Repositories
sneha-nishtala/boston_housing
The Boston housing market is highly competitive, and you want to be the best real estate agent in the area. To compete with your peers, you decide to leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home. Luckily, you’ve come across the Boston Housing dataset which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. Your task is to build an optimal model based on a statistical analysis with the tools available. This model will then be used to estimate the best selling price for your clients' homes.
sneha-nishtala/Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
sneha-nishtala/finding_donors
In this project, I apply supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause. I first explored the data to learn how the census data is recorded. Next, I applied a series of transformations and preprocessing techniques to manipulate the data into a workable format. I then evaluated several supervised learners, and considered the best suited for the solution. Then, I optimized the model and finally, explored the chosen model and its predictions under the hood, to see just how well it's performing when considering the data it's given.
sneha-nishtala/titanic_survival_exploration
This is the first project (p0) of Udacity's machine learning nanodegree. It is implementation of the decision tree algorithm to accurately predict survival of passengers on the titanic ship.