A repository of my activities, jupyter notebooks, datasets used in machine learning algorithms for data science
- Linear Regression
- Neural Networks
- Machine Learning in JavaScript
- Exploratory Data Analysis
- E2E Web App
- Natural Language Processing
- Classification
Linear regression is one of the most basic forms of machine learning and is used to predict numeric values. This section is based on the following documentation: https://www.tensorflow.org/tutorials/estimator/linear
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California House Price Prediction - This project includes exploratory data analysis, intensive data visualization, data feature engineering, and selecting features based on the correlation of the features.
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Simple Linear Regression from Scratch - Simple linear regression is an approach for predicting a response using a single feature. This project does not utilize machine learning libraries aside from computation libraries (numpy and pandas).
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Stock Price Predictor - This project predicts stock prices of natural gas, facebook, and USD-PHP currency using linear regression. The datasets are from yahoo finance library.
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
- Feedforward NN Diabetes dataset - Feedforward neural network is a type of artificial neural network in which nodes' connections do not form a loop.
In statistics, exploratory data analysis is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods.
Classification is a process of categorizing a given set of data into classes, It can be performed on both structured or unstructured data. The process starts with predicting the class of given data points.