Discover @sidhu1012 Open Source projects

Sudeep Sidhu (@sidhu1012)

sidhu1012

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Registered almost 6 years ago
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Location 🇮🇳 India
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Insurance-Price_Predictor

Regression model to predict insurance price of indivduals

Jupyter Notebook

Movie-Recommender

Content Based Recommender SystemNow, let's take a look at how to implement Content-Based or Item-Item recommendation systems. This technique attempts to figure out what a user's favourite aspects of an item is, and then recommends items that present those aspects. In our case, we're going to try to figure out the input's favorite genres from the movies and ratings given.

Telecoumminication

Imagine a telecommunications provider has segmented its customer base by service usage patterns, categorizing the customers into four groups. If demographic data can be used to predict group membership, the company can customize offers for individual prospective customers. It is a classification problem. That is, given the dataset, with predefined labels, we need to build a model to be used to predict class of a new or unknown case

Fake-News-Analyzer

Fish-Prediction

Predecits species of fish using fish dimensions

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Credit-Card-Fraud-Detection

Countdown-timer

sidhu1012.github.io

This is my personal blog to track and share my GSoC progress.

DataScientist

BitCoin-Predcitor

Iris-Classification

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Hand-Written-Digit-Classification

Classifies which handwritten digit it is

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sidhu1012

co2emission

get co2 emmision of engine from engine details

Image-Classifier-

Uses keras CNN model to classify images , tensorflow at backend

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Cloud-Kitchen

finds best location to open swiggy/zomato like office in a city

Identify-behavior-patterns-via-smart-phone-sensor-data

Identifying Human Behavior patterns using data recorded from smartwatch

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Movie-Recommender-Collaborative-

Magic-Calculator

Traffic-Sign-Classification

CNN model which classifies traffic sign images

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Stenography

Encode and Decode your text messages

Cement-Strength-

Regression through layers using keras

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Data-Analysis

Data Anlysis NB

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Color-Color

In this game player has to enter color of the word that appears on the screen and hence the score increases by one, the total time to play this game is 30 seconds. Colors used in this game are Red, Blue, Green, Pink, Black, Yellow, Orange, White, Purple and Brown. Interface will display name of different colors in different colors. Player has to identify the color and enter the correct color name to win the game.

News_Virality

Crawl news & information websites & anticipate the likelihood of its virality.

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SoloG

Rain-Estimator

Predicts rain estimation on daily basis based on pert rain records

Clustering-on-Vehicle-dataset

Imagine that an automobile manufacturer has developed prototypes for a new vehicle. Before introducing the new model into its range, the manufacturer wants to determine which existing vehicles on the market are most like the prototypes--that is, how vehicles can be grouped, which group is the most similar with the model, and therefore which models they will be competing against. Our objective here, is to use clustering methods, to find the most distinctive clusters of vehicles. It will summarize the existing vehicles and help manufacturers to make decision about the supply of new models.

customer-segmentation-

Imagine that you have a customer dataset, and you need to apply customer segmentation on this historical data. Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. For example, one group might contain customers who are high-profit and low-risk, that is, more likely to purchase products, or subscribe for a service. A business task is to retaining those customers. Another group might include customers from non-profit organizations. And so on.

The-Best-Classifier

You load a historical dataset from previous loan applications, clean the data, and apply different classification algorithm on the data. You are expected to use the following algorithms to build your models: k-Nearest Neighbour Decision Tree Support Vector Machine Logistic Regression The results is reported as the accuracy of each classifier, using the following metrics when these are applicable: Jaccard index F1-score LogLoass

Calculator-GUI-

UI based simple calculator using Python Tkinter module, which can perform basic arithmatic operations addition, subtraction, multiplication and division.

Jupyter Notebook

Drug-Assignment

About the dataset Imagine that you are a medical researcher compiling data for a study. You have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y. Part of your job is to build a model to find out which drug might be appropriate for a future patient with the same illness. The feature sets of this dataset are Age, Sex, Blood Pressure, and Cholesterol of patients, and the target is the drug that each patient responded to.

Pulsar_Star-Predictor

Pulsars are a rare type of Neutron star that produce radio emission detectable here on Earth. They are of considerable scientific interest as probes of space-time, the inter-stellar medium, and states of matter . As pulsars rotate, their emission beam sweeps across the sky, and when this crosses our line of sight, produces a detectable pattern of broadband radio emission. As pulsars rotate rapidly, this pattern repeats periodically. Thus pulsar search involves looking for periodic radio signals with large radio telescopes. Each pulsar produces a slightly different emission pattern, which varies slightly with each rotation . Thus a potential signal detection known as a 'candidate', is averaged over many rotations of the pulsar, as determined by the length of an observation. In the absence of additional info, each candidate could potentially describe a real pulsar. However in practice almost all detections are caused by radio frequency interference (RFI) and noise, making legitimate signals hard to find. Machine learning tools are now being used to automatically label pulsar candidates to facilitate rapid analysis. Classification systems in particular are being widely adopted, which treat the candidate data sets as binary classification problems. Here the legitimate pulsar examples are a minority positive class, and spurious examples the majority negative class.

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Gravity

Sympy for solving gravitational force

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Logistic-Telecommunication-

About the dataset We will use a telecommunications dataset for predicting customer churn. This is a historical customer dataset where each row represents one customer. The data is relatively easy to understand, and you may uncover insights you can use immediately. Typically it is less expensive to keep customers than acquire new ones, so the focus of this analysis is to predict the customers who will stay with the company. This data set provides information to help you predict what behavior will help you to retain customers. You can analyze all relevant customer data and develop focused customer retention programs. The dataset includes information about: Customers who left within the last month – the column is called Churn Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies Customer account information – how long they had been a customer, contract, payment method, paperless billing, monthly charges, and total charges Demographic info about customers – gender, age range, and if they have partners and dependents