Sunil M Anirudhan

Project Analysis

FIFA 19 Player Value Prediction & Recruitment Strategy

We decided to analyze the players' data from the game FIFA 19. There is a lot of several players from different countries, playing in different competitions. Their abilities in the game should reflect their real-world skills. The game's creators are about its efforts by creating game attributes, such as sprint speed, force bullets, endings or headers that we can express numerically. In the project, we will try to classify players into a game room based on these attributes, and positions and predict their market value in the game. Since the game does not always show players the market value of the player, but only its attributes, our model can be useful in determining the amount offered for the transfer of the player when negotiating in the game. When in the real world the abilities of the players (e.g., ending) do not bear any numerical. We will try to use the fact that in the game of expression, we can find out which attributes are important for certain gaming positions.

Spotify

<h5>Usage</h5> The dataset can be used for: <ul> <li>Building a Recommendation System based on user input or preference.</li> <li>Classification purposes based on audio features and available genres.</li> <li>Any other application that you can think of. Feel free to discuss!</li> </ul> <h2>Column Description</h2> <ul> <li>track_id: The Spotify ID for the track.</li> <li>artists: The names of the artists who performed the track. If there is more than one artist, they are separated by a semicolon (;).</li> <li>album_name: The album name in which the track appears.</li> <li>track_name: The name of the track.</li> <li>popularity: A value between 0 and 100 indicating the popularity of a track. It is calculated based on the number of plays and recency of plays.</li> <li>duration_ms: The length of the track in milliseconds.</li> <li>explicit: Whether the track has explicit lyrics (true = yes, false = no or unknown).</li> <li>danceability: A measure (0.0 to 1.0) of how suitable a track is for dancing, based on musical elements like tempo and beat strength.</li> <li>energy: A measure (0.0 to 1.0) of intensity and activity in a track. Higher values indicate fast, loud, and noisy tracks.</li> <li>key: The musical key of the track, mapped to standard Pitch Class notation (e.g., 0 = C, 1 = C♯/D♭, 2 = D, etc.).</li> <li>loudness: The overall loudness of a track in decibels (dB).</li> <li>mode: Indicates the modality of a track (1 = Major, 0 = Minor).</li> <li>speechiness: Detects the presence of spoken words in a track. Values above 0.66 indicate mostly speech-based tracks.</li> <li>acousticness: A confidence measure (0.0 to 1.0) of whether the track is acoustic. Higher values indicate acoustic tracks.</li> <li>instrumentalness: Predicts whether a track contains no vocals. Values closer to 1.0 indicate instrumental tracks.</li> <li>liveness: Measures the presence of an audience in the recording. Values above 0.8 suggest a live performance.</li> <li>valence: A measure (0.0 to 1.0) of the musical positiveness of a track. Higher values indicate happier and more cheerful tracks.</li> <li>tempo: The estimated tempo of a track in beats per minute (BPM).</li> <li>time_signature: An estimated time signature, ranging from 3 to 7 (e.g., 3/4 to 7/4).</li> <li>track_genre: The genre of the track.</li> </ul>

HR Salary Prediction

The HR salary dataset from Kaggle is a valuable resource for analyzing employee compensation trends. It contains information on employee demographics, job-related details, and compensation figures. This data can be used to identify factors that influence salary, such as experience, education, and job title, and assess organisational pay equity.

Spam Detection Application

This project aims to develop an intelligent spam detection system using deep learning techniques. The objective is to accurately classify emails as either "spam" or "not spam" by building and deploying a robust model. The project involves several stages, starting with dataset acquisition, followed by data preprocessing, model training, and evaluation. The best-performing model and preprocessing pipeline are then saved and integrated into an interactive web application using Django and Streamlit.

Predict Iris

AI-Enhanced Technical Indicator-Based Stock Trading Strategy

Stock market forecasting often suffers from inconsistency due to noisy data, emotional trading, and the isolated use of indicators or sentiment. Traders lack a unified system that combines technical signals, sentiment analysis, and AI models to generate high-confidence trade decisions. Traditional methods based on single indicators (like MACD or RSI alone) often fail in volatile or uncertain markets.

YOLOv8-Based Object Detection & Segmentation for Trash

Waste classification and plant disease identification are critical for sustainability and agriculture. Manual sorting is time-consuming, error-prone, and inconsistent. Automating these tasks requires accurate object detection and segmentation, even in noisy, real-world environments with class imbalance and complex backgrounds.

GAN Model for MNIST Digit Generation

Generating realistic handwritten digit images requires a model capable of learning complex data distributions from limited training samples. Traditional image generation methods often lack diversity and tend to overfit the dataset. <div class="mb-16 fw-bold">Project Challenge</div> The key challenge is to train a GAN (Generative Adversarial Network) that can: <ul class="text-secondary-light" style="list-style-type: disc; padding-left: 20px;"> <li>Generate sharp and visually diverse handwritten digits</li> <li>Maintain training stability throughout epochs</li> <li>Avoid common pitfalls such as mode collapse and vanishing gradients</li> </ul>

Stable Diffusion-Based Image Generation and Prompt Engineering

<div class="mb-16 fw-bold">🧩 Problem Statement</div> Generating high-quality, realistic, and stylistically accurate images using text prompts is a complex task. Models like Stable Diffusion can produce widely varied outputs depending on multiple generation parameters. <div class="fw-bold mt-3">Key Factors Influencing Output</div> <ul class="text-secondary-light" style="list-style-type: disc; padding-left: 20px;"> <li>Prompt wording: Subtle changes can drastically affect composition and detail</li> <li>Negative prompts: Used to suppress unwanted elements</li> <li>Scheduler selection: Influences the image generation process (e.g., DDIM, Euler)</li> <li>CFG Scale: Controls the strength of prompt adherence (higher = more literal)</li> <li>Inference steps: Affects image quality, detail, and noise reduction</li> </ul> Understanding how each parameter impacts the output is critical for controlling quality, style, and realism in AI art and computer vision applications.

CIFAR-10 Image Classification with CNN & Transfer Learning

<div class="mb-16 fw-bold">🚨 Real-World Challenges in Image Classification</div> <ul class="text-secondary-light" style="list-style-type: disc; padding-left: 20px;"> <li>Small input size: CIFAR-10 images are only 32×32, limiting detail and context</li> <li>Overfitting: Models often memorize training data rather than generalize</li> <li>Poor robustness: Struggle with varied lighting, backgrounds, blur, or webcam noise</li> <li>Deployment hurdles: Real-time webcam predictions suffer from latency and performance issues, especially on low-resource hardware</li> </ul>

Recommender System for Amazon Beauty Products

E-commerce platforms like Amazon face the challenge of helping users navigate massive product catalogs. Without personalization, users experience choice overload and irrelevant suggestions. Many products lack complete metadata, and cold-start users/items limit collaborative approaches.

Personal Assistant Chatbot – Rule-Based + NLP Hybrid

Users often need a centralized virtual assistant that can handle small tasks like reminders, greetings, simple queries (date/time/weather), or custom commands. Existing solutions (like Alexa or Siri) are either voice-only or not customizable for personal needs, and most bots cannot be extended easily by developers.

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Project Analysis