IshanSingh611/Geely-Auto-Price-Prediction-Linear-Regression

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IshanSingh611/Geely-Auto-Price-Prediction-Linear-Regression

IshanSingh611

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A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts

Popularity-Based-Recommendation-System-User-Cold-Start-Problem

What will happen if a new user or a new item is added in the dataset?

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Item-Based-Collaborative-Filtering

There is one famous quote about customer relationship. The summary of the quote will go like this "Customers don't know what they want until we show them." So, recommendations engines will help customers to find information, product & services they might not have thought of.

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Lending-Club-Case-Study

You work for a consumer finance company which specializes in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile

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User-Based-Collaborative-Filtering

There is one famous quote about customer relationship. The summary of the quote like this <b>"Customers don't know what they want until we show them."</b> So Recommendation Systems will help customers to find information, product & services they might not have thought of.

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Exploratory-Data-Analytics-of-Sachin-Tendulkar-ODI-with-Plotly

Exploratory Data Analytics is an approach, or a philosophy, which seeks to explore the most important and often hidden patterns in a data set. Statisticians use it to take a bird‟s eye view of the data and try to make some sense of it.

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Spark-Funds-Investments-Assignment

Spark Funds wants to make investments in a few companies. The CEO of Spark Funds wants to understand the global trends in investments so that she can take the investment decisions effectively.

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Gradient-Descent-and-Cost-Function-Animation-using-Celluloid

Gradient descent is an optimization algorithm used to find the values of parameters (coefficients) of a function (f) that minimizes a cost.

Telecom-Customer-Churn-Prediction

Telecom Customer Churn with Stats Models

Jupyter Notebook

Media-Company-Case-Study

A digital media company (similar to Voot, Hotstar, Netflix, etc.) had launched a show. Initially, the show got a good response, but then witnessed a decline in viewership. The company wants to figure out what went wrong.

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Telecom-Churn-Prediction-including-PCA

In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate and moreover, it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition

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Visual-Cryptography-GUI-Tool

Visual Cryptography is a cryptographic technique which allows visual information pictures, text, etc.) to be encrypted in such a way that the decrypted information appears as a visual image. where an image was broken up into N number of shares so that only someone with all N shares could decrypt the image, while any N − 1 shares revealed no information about the original image. Each share was printed on a separate transparency, and decryption was performed by overlaying the shares. When all n shares were overlaid, the original image would appear. The basic principle of the visual cryptography scheme (VCS) was first introduced by Naor and Shamir. VCS is a kind of secret sharing scheme that focuses on sharing secret images.

IshanPy

Bengaluru-House-Price-Prediction

Bengaluru House Price Prediction

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HMMs-Based-POS-Tagging-using-Modified-Viterbi-algorithm

In this project, we need to modify the Viterbi algorithm to solve the problem of UNKNOWN WORDS using at least two techniques. Though there could be multiple ways to solve this problem, we may use the following hints: 1. Which tag class do we think most unknown words belong to? Can we identify rules (e.g. based on morphological cues) that can be used to tag unknown words? You may define separate python functions to exploit these rules so that they work in tandem with the original Viterbi algorithm. 2. Why does the Viterbi algorithm choose a random tag on encountering an unknown word? We have to modify the Viterbi algorithm so that it considers only one of the transition or emission probabilities for unknown words. In original Viterbi algorithm there is a loss of accuracy was majorly due to the fact that when the algorithm encountered an unknown word (i.e. not present in the training set, such as 'Twitter'), it assigned an incorrect tag arbitrarily. This is because, for unknown words, the emission probabilities for all candidate tags are 0, so the algorithm arbitrarily chooses (the first) tag. For this Project , we’ll use the Treebank dataset of NLTK with the 'universal' tagset. The Universal tagset of NLTK comprises only 12 coarse tag classes as follows: Verb, Noun, Pronouns, Adjectives, Adverbs, Adpositions, Conjunctions, Determiners, Cardinal Numbers, Particles, Other/ Foreign words, Punctuations. Note that using only 12 coarse classes (compared to the 46 fine classes such as NNP, VBD etc.) will make the Viterbi algorithm FASTER as well.

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