Aashutosh Kuikel's Work | ContraWork by Aashutosh Kuikel
Aashutosh Kuikel

Aashutosh Kuikel

AI/ML developer specializing in Python, machine learning,

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Kathmandu 44600, Nepal

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Kathmandu 44600, Nepal

Cover image for Python Chatbot Using Cosine Similarity I
Python Chatbot Using Cosine Similarity I built a Python-based chatbot that uses cosine similarity to compare text and identify the closest matching response. The project explores text similarity, vector-based comparison, and conversational interaction, demonstrating how mathematical techniques can be applied to build a simple chatbot. Key Features Text similarity matching: Uses cosine similarity to compare text representations. Response selection: Identifies the closest matching candidate based on similarity scores. Python development: Implements the chatbot's matching logic using Python. Applied mathematics: Uses vector similarity to compare text representations. Technical Approach Cosine similarity measures the angle between two vectors to estimate how closely they align. When combined with an appropriate text vectorization method, it can help a chatbot match user messages with relevant stored responses. Technologies Python · Natural Language Processing (NLP) · Cosine Similarity Project Source View the project on GitHub (https://github.com/ak-Machine-Learning/Chatbot)
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Cover image for Sentiment Analysis Using Machine Learning
Sentiment Analysis Using Machine Learning and Deep Learning I developed a text-classification project to compare traditional machine learning algorithms with a deep learning model for sentiment analysis. The project covers text preprocessing, feature extraction, model training, performance evaluation, and saving trained models for reuse. What I Built Text preprocessing: Prepared review text for model training. Feature extraction: Used TF-IDF for traditional machine learning models and tokenized sequences for the GRU model. Model comparison: Evaluated Logistic Regression, Multinomial Naive Bayes, Linear SVM, Random Forest, and GRU. Performance evaluation: Generated classification reports and visualizations to compare model results. Model persistence: Saved trained models and preprocessing components for reuse. Tech Stack Python · Pandas · NumPy · Scikit-learn · TensorFlow/Keras · NLP · TF-IDF · Matplotlib · Joblib What This Project Demonstrates This project gave me practical experience building and evaluating NLP pipelines, comparing different modelling approaches, and examining how model choice affects text-classification performance. Source Code View the project on GitHub (https://github.com/ak-Machine-Learning/Sentiment-Analysis-Model-Comparison)
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Cover image for Python,AI/ML and full stack
Python,AI/ML and full stack . Please visit my github for detailed overview :https://github.com/aKmsdfhjb
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Cover image for Doodle Recognition — 5-Class CNN An
Doodle Recognition — 5-Class CNN An interactive computer vision project that classifies hand-drawn sketches into five categories using a Convolutional Neural Network (CNN) trained on Google's Quick, Draw! dataset. Overview The project explores how deep learning can recognize simple drawings from their visual patterns. A CNN learns features such as edges, curves, and shapes to distinguish between five classes: Apple Cookie Dog Eye Face The goal is to connect model training with an interactive drawing experience, allowing users to submit sketches and receive a predicted class. Technical Approach Data preparation: Process the drawing data into model-ready arrays and corresponding class labels. Model development: Train a CNN using TensorFlow/Keras for five-class image classification. Inference: Preprocess user drawings consistently with the training data before passing them to the trained model. User interface: Provide a drawing canvas and display the model's predicted class. Tech Stack Python TensorFlow / Keras NumPy Google Quick, Draw! dataset What This Project Demonstrates CNN-based image classification Image preprocessing and labelled dataset preparation Multi-class prediction Integration of a trained model into an interactive application Separation of model training, inference, and user-interface components Source Code GitHub repository (https://github.com/aKmsdfhjb/Doodle)
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