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Srishti Patkar's Projects

cancer-prediction-using-svm icon cancer-prediction-using-svm

In this notebook, you will use SVM (Support Vector Machines) to build and train a model using human cell records, and classify cells to whether the samples are benign or malignant.

churn-prediction-using-logstic-regression icon churn-prediction-using-logstic-regression

In this notebook, you will learn Logistic Regression, and then, you'll create a model for a telecommunication company, to predict when its customers will leave for a competitor, so that they can take some action to retain the customers.

co2-emission-prediction icon co2-emission-prediction

A simple machine learning model to predict the CO2 emission using a fuel consumption dataset which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale.

customer-classification-knn icon customer-classification-knn

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. The example focuses on using demographic data, such as region, age, and marital, to predict usage patterns. The target field, called custcat, has four possible values that correspond to the four customer groups, as follows: 1- Basic Service 2- E-Service 3- Plus Service 4- Total Service Our objective is to build a classifier, to predict the class of unknown cases. We will use a specific type of classification called K nearest neighbour.

demographic-data-analyzer icon demographic-data-analyzer

Analysing demographic data using Pandas using a dataset of demographic data that was extracted from the 1994 Census database.

denspart icon denspart

Atoms-in-molecules density partitioning schemes based on stockholder recipe

drug-prediction-decision-tree icon drug-prediction-decision-tree

In this lab exercise, you will learn a popular machine learning algorithm, Decision Trees. You will use this classification algorithm to build a model from the historical data of patients, and their response to different medications. Then you will use the trained decision tree to predict the class of an unknown patient, or to find a proper drug for a new patient.

extracting-and-visualizing-stock-data icon extracting-and-visualizing-stock-data

Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.

hacktoberfest_2020 icon hacktoberfest_2020

Solve the given questions, and submit a PR.💬 Make sure you submit the solution in the correct folder. ✔

movie-recommendation-engine icon movie-recommendation-engine

A recommendation engine is a type of data filtering tool using machine learning algorithms to recommend the most relevant items to a particular user or customer. These systems have become ubiquitous, and can be commonly seen in online stores, movies databases and job finders. This is a content-based recommendation engine

password-validation-program icon password-validation-program

A program that takes in a string as input and evaluates it as a valid password. The password is valid if it has at a minimum 2 numbers, 2 of the following special characters ('!', '@', '#', '$', '%', '&', '*'), and a length of at least 7 characters.

tic-tac-toe icon tic-tac-toe

It is a two-player game. After the game is finished you can choose to start a new game or exit.

voice-activity-detection-using-deep-learning icon voice-activity-detection-using-deep-learning

This project shows how to detect regions of speech in a low signal-to-noise environment using deep learning. The project uses the Speech Commands Dataset to train a Bidirectional Long Short-Term Memory (BiLSTM) network to detect voice activity.

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