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assignment_random_forest_fraud_check

Use Random Forest to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"

data-science-assignment-reunion-

A person’s creditworthiness is often associated (conversely) with the likelihood they may default on loans.

duplicate_question_pair

Quora and Stack Exchange are knowledge-sharing platforms where people can ask questions in the hopes of attracting high-quality answers. Often, questions that people submit have previously been asked. Companies like Quora can improve user experience by identifying these duplicate entries. This would enable users to find questions that have already been answered and prevent community members from answering the same question multiple times. Consider the following pair of questions: Is talent nurture or nature? Are people talented by birth or can it be developed? These are duplicates; they are worded differently, but they have the same intent. This blog post focuses on solving the problem of duplicate question identification.

effectiveness-of-std-drug

How effective is the STD drug based on Reviews?

incident_impact_prediction

To predict the impact of the incident raised by the customer.

laptop-price-prediction-machine-learning-project

I will make a project for Laptop price prediction. The problem statement is that if any user wants to buy a laptop then our application should be compatible to provide a tentative price of laptop according to the user configurations. Although it looks like a simple project or just developing a model, the dataset i have is noisy and needs lots of feature engineering, and preprocessing that will drive your interest in developing this project.

line-detection-text-detection-object-detection

1)line detection using HoughLines opencv library 2)text detection using Python-tesseract OCR Engine 3)object detection using YOLOV5

logistic-regression-assignment

Output variable -> y y -> Whether the client has subscribed a term deposit or not Binomial ("yes" or "no") Attribute information For bank dataset Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services") 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown","secondary","primary","tertiary") 5 - default: has credit in default? (binary: "yes","no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes","no") 8 - loan: has personal loan? (binary: "yes","no") # related with the last contact of the current campaign: 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric) # other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success") Output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes","no") 8. Missing Attribute Values: None

obi-assingment

ocr

posenet-demo-ml5js

A posenet demo built using ml5.js

simple-linear-regression-assignment

Assignment-Simple-Linear-Regression-1. Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building.