Repository containing portfolio of machine learning projects completed by me for academic and self learning purposes. Presented in the form of iPython Notebooks.
The sinking of the Titanic is one of the most infamous shipwrecks in history.On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.I have built a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).
https://github.com/ria3999/mahine_learning-codes/blob/master/titanic.py
This dataset is related to red variants of the Portuguese “Vinho Verde” wine. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.). The input features are as follows: fixed acidity - most acids involved with wine or fixed or nonvolatile (do not evaporate readily); volatile acidity - the amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste; citric acid - found in small quantities, citric acid can add ‘freshness’ and flavor to wines; residual sugar - the amount of sugar remaining after fermentation stops, it’s rare to find wines with less than 1 gram/liter, and wines with greater than 45 grams/liter are considered sweet; chlorides - the amount of salt in the wine; free sulfur dioxide - the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine; total sulfur dioxide - the amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine; density - the density of water is close to that of water depending on the percent alcohol and sugar content; pH - describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale; sulfates - a wine additive which can contribute to sulfur dioxide gas (S02) levels, which acts as an antimicrobial and antioxidant alcohol - the percent alcohol content of the wine; The output feature is:
quality - output variable (based on sensory data, the score between 0 and 10)
https://github.com/ria3999/Machine_Learning-Portfolio/blob/master/red_wine.ipynb
Dream Housing Finance company deals in all home loans. They have a presence across all urban, semi-urban, and rural areas. Customer-first applies for a home loan after that company validates the customer eligibility for the loan. Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, we have to identify the customer segments, those are eligible for loan amount so that they can specifically target these customers.
https://github.com/ria3999/Machine_Learning-Portfolio/blob/master/credit_risk.ipynb
HR analytics is revolutionizing the way human resources departments operate, leading to higher efficiency and better results overall. Human resources have been using analytics for years. However, the collection, processing, and analysis of data have been largely manual, and given the nature of human resources dynamics and HR KPIs, the approach has been constraining HR. Therefore, it is surprising that HR departments woke up to the utility of machine learning so late in the game. Here we have to use predictive analytics in identifying the employees most likely to get promoted.
https://github.com/ria3999/Machine_Learning-Portfolio/blob/master/HR_ANALYTICS.ipynb
In this, we have to predict the number of positive and negative reviews based on sentiments by using different classification models.
https://github.com/ria3999/Machine_Learning-Portfolio/blob/master/Sentiment_imdb_.ipynb
A basic effort for understanding how a chatbot works. This chatbot has been made using python.
https://github.com/ria3999/Machine_Learning-Portfolio/blob/master/Chatbot.ipynb
Recommendation based on movie review
Recommendation of books based on users interest
https://github.com/ria3999/Machine_Learning-Portfolio/blob/master/BookRecommendationSystem.ipynb