tonmoy-khanal Goto Github PK

followers: 4.0 following: 3.0 repos: 26.0 gists: 0.0

Name: Tonmoy Khanal

Type: User

Twitter: tonmoy__khanal

Location: Bengaluru

Blog: https://tonmoykhanal.my.canva.site/

Hi there 👋 I am Tonmoy Khanal

🔭 I am good at Machine learning and Deep learning. I use python for Data science.
🔭 I have completed my course completion in Data Science from GUVI GEEK NETWORK PRIVATE LIMITED (IITM)

Area of Interest 💼 :

Language and Tools 🧰:

Database Known ☁️:

Github Stats 📈:

Top Languages

GitHub Streak

Contributions

Reach me out 🤙:

Tonmoy Khanal's Projects

chennai_house_price_prediction_

This Project is to help buyers to identify a fair price for the home and also give insights to sellers as to how to build homes that fetch them more money. Submitted as Project to GUVI (IITM)

The model classifies the population into four mutually exclusive groups: susceptible (at risk of contracting the disease), exposed (infected but not yet infectious), infectious (capable of transmitting the disease), and removed (those who recover or die from the disease). A susceptible individual can become exposed only through contact with some infectious person. Susceptible individuals first enter the exposed stage, during which they may have a low level of infectivity; they become infectious thereafter. The infection rate represents the probability of transmission from an infectious person to a susceptible one. The incubation rate (the reciprocal of the average duration of incubation) is the rate at which latent individuals become infectious; and the removal rate is the reciprocal of the average duration of infection. The basic reproduction number (BRN) is the expected number of cases directly generated by one case. A BRN greater than one indicates that the outbreak is self-sustaining, while a BRN less than one indicates that the number of new cases decreases over time and eventually the outbreak will stop. Ideally, the BRN should be reduced in order to slow down an epidemic.

credit-card-default-prediction

This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005. Submitted to GUVI (IITM)

crypto-currency-price-prediction

Cryptocurrency Price Prediction Using LSTM neural network

customer-churn-prediction

Built a churn prediction model to retain subscription customers. Expertly preprocessed data, engineered features, and optimized models for accuracy. Deployed the model via Flask for real-time predictions, showcasing end-to-end data science skills.

e-commerce-customer-segmentation

Demand Forecasting of products Demand Forecasting is the art & science of predicting customer’s future demand for products.

e-commerce-customer-segmentation-using-nlp

Analysing the content of an E-commerce database that contains list of purchases. Based on the analysis, I develop a model that allows to anticipate the purchases that will be made by a new customer, during the following year from its first purchase.

fake-news-detection

forage-jpmc-swe-task-1

Starter repo for task 1 of the JPMC software engineering program

hand-gesture-controlled-robot-iot

In this project, we have designed a simple Hand Gesture Controlled Robot using Arduino. This Hand Gesture Controlled Robot is based on Arduino Nano, MPU6050, RF Transmitter-Receiver Pair and L293D Motor Driver. Even though the title says it as a Hand Gestured Controlled Robot, technically this robot is controlled by the tilt of the hand.

image-captioning-using-keras

The dataset contains over 82,000 images, each of which has at least 5 different caption annotations. The code below downloads and extracts the dataset automatically. Warning: File Size 1.3GB Time consuming

iot-projects

Explore innovative IoT projects in this showcase. Discover smart home automation, environmental monitoring, healthcare solutions, and more. Dive into code, hardware, and documentation to kickstart your IoT journey.

keyboard-and-mouse-controller-iot

This program illustrates the use of the Mouse and Keyboard libraries together. Five momentary switches act as directional buttons for your cursor. When a button is pressed, the cursor on your screen will move, and a keypress, corresponding to the letter associated with the direction, will be sent to the computer. Once we have the Leonardo programmed and wired up, open up the Arduino IDE and See the Magic.

login-register-python

Registration and Login system with Python, file handling STAGE -- 1 Registration When the user chooses to Register ---> email/username should have "@" and followed by "." eg:- [email protected] [email protected] ---> it should not be like this eg:- @gmail.com there should not be any "." immediate next to "@" [email protected] it should not start with special characters and numbers ---> password (5 < password length > 16) Must have minimum one special character, one digit, one uppercase, one lowercase character Stage 2 Once the username and password are validated, store those values in a file Stage 3 Login When the user chooses to Login, check whether his/her credentials exist in the file or not based on the user input If doesn’t exist ask them to go for Registration or If they have chosen forget password you should be able to retrieve their original password based on their username provided in the user input If nothing matches in your file you should ask them to Register

sales-data-analysis-project

Analyze sales data for trends, top products, revenue metrics. Extract insights, create visuals. Drive data-driven sales strategies.

sentiment-analysis

The first project of this list is to build a machine learning model that predicts the sentiment of a movie review. Sentiment analysis is an NLP technique used to determine whether data is positive, negative, or neutral. It’s really helpful for businesses because it helps understand the overall opinions of their customers. For this project, you will use an IMDB dataset that contains 50k movie reviews. with 2 columns (review and sentiment). The goal is to build the best machine learning model that predicts the sentiment given a movie review. To make this project beginner friendly you only have to predict whether a movie review is positive or negative. This is known as binary text classification because there are only two possible outcomes.

serp-crawler

● Create a web crawler using python that is able to scrape first 10,000 results for the following google search input - site:youtube.com openinapp.co ● Collect all the given YouTube links of the above search link. ● Give the results in the JSON/CSV format.