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Ravikumar.A
Agriculture plays a crucial role in the economic stability of many nations, and optimizing crop selection is essential for enhancing agricultural productivity and sustainability. The "Crop Recommender System Using Machine Learning Approach" aims to leverage machine learning techniques to provide precise crop recommendations based on various environmental and soil conditions. By incorporating factors such as soil composition, pH level, temperature, humidity, rainfall, and geographic location, this system suggests the most suitable crops for a given area. The system utilizes machine learning models, particularly Random Forest and Decision Trees, to analyze historical agricultural data, predict optimal crops, and improve the decision-making process for farmers. By training the model on large datasets, it ensures accurate predictions that align with real-world agricultural practices. The application of this system can lead to higher crop yields, sustainable farming practices, and reduced risks associated with poor crop choices. Through rigorous evaluation using standard classification metrics, the model's performance demonstrates its potential to revolutionize farming practices by aiding farmers in making informed decisions. The system has the potential to be an invaluable tool for agricultural consultants, farmers, and policymakers, ensuring long-term sustainability and improved productivity
Crop Recommendation, Machine Learning, Random Forest, Decision Trees, Agriculture, Sustainability, Yield Prediction, Soil Composition, Environmental Factors, Data Analytics.
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R.Senthilkumar
Fraudulent activities in insurance claims have become a significant challenge for the insurance industry, leading to substantial financial losses annually. This project, titled "Fraud Detection and Analysis for Insurance Claim using Machine Learning," aims to develop a robust and efficient system to identify and analyze fraudulent claims. The system leverages machine learning techniques to analyze patterns, anomalies, and inconsistencies in claim data, enabling early detection of potentially fraudulent activities. Key features of this system include data preprocessing to handle missing or inconsistent information, feature selection to identify critical indicators of fraud, and model training using algorithms such as Random Forest, Logistic Regression, and Gradient Boosting. The model is trained on historical claim data to achieve high accuracy in distinguishing fraudulent claims from legitimate ones. Performance evaluation metrics such as accuracy, precision, recall, and F1-score are employed to assess the system's effectiveness. Additionally, the project incorporates advanced techniques like Natural Language Processing (NLP) to analyze claim narratives and identify suspicious patterns. Visualization tools are also integrated to provide insights into the nature of detected fraud and enhance decision-making for insurance analysts.The proposed solution not only minimizes financial losses but also improves the operational efficiency of insurance companies by automating fraud detection processes. T
Keywords: Insurance Fraud Detection, Machine Learning, Fraudulent Claims Analysis
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