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Agriculture is vital for survival in India. Rainfall is crucial to agriculture. Predicting rainfall has become a significant issue recently. Rainfall forecasting helps people be prepared and informed of impending rain so they can take the necessary safety measures to preserve their crops from the rain. There are numerous methods available to predict rainfall. Predicting rainfall is where machine learning techniques are most beneficial. XGBoost, Decision Tree, Random Forest, Light BGM, and Logistic Regression are some of the most important machine learning algorithms. The linear and non-linear models, which are both often used, forecast seasonal precipitation. Logistic regression models are the initial models. Rainfall can be predicted when utilising Artificial Neural Networks (ANN) by employing Back Propagation Neural Networks, Decision Trees, and regression models like Random Forest. Due to the atmosphere's dynamic character, applied mathematics techniques are unable to guarantee reliable precision for a statement about precipitation. Regression may be used in the prediction of precipitation utilising machine learning approaches. The goal of this project is to provide non-experts with simple access to the methods and approaches used in the field of precipitation prediction as well as to compare different machine learning techniques. [ABSTRACT FROM AUTHOR]

India depends substantially on agriculture. Agriculture output is impacted by seasonal, organic, and economic factors. The current demographics of our country make it challenging to estimate agricultural production. Long-term crop output forecasts enable farmers to prepare for activities such as selling and storage. Given the huge amount of data involved in crop output predictions, data mining techniques are the right approach. An approach known as data mining serves to harvest anticipated information from enormous datasets that have never been accessed previously. Data mining, which helps organizations foresee future trends and behavior, decisions may be made with knowledge. This research uses the Random Forest approach to provide a brief overview of the agriculture yield estimate for a certain area. [ABSTRACT FROM AUTHOR]

After a deep questioner among students, faculty members and higher education experts. The demand for qualified engineers in the specific field as understandably gone down. The situation is grimmer for tier-2 and tier-3 colleges in India. The purpose of opting engineering course is not to get expertise in the particular course, instead to get a job in government sector with valid degree. The problem identified in the field of engineering education towards improving the quality in education is addressed with the help of exploratory data analysis. The dataset used in our experiment is collect from UCI machine Learning Repository having 33 variables and 1044 observations. The contribution made in the paper is to identify the vital attributes using single and multi-variant regression techniques. To perform prediction using Decision Tree, Random Forest, Support vector Machine and compare their performance in terms of classification accuracy and F-Score. In addition to that a convolution Neural Network (CNN) model is established in which, the vital attributes identified using regression techniques are provided as inputs and weights at each stage is estimated using Gradient Decent algorithm with step size 0.5. The classification accuracy from 63% is improved to 97% with the help of CNN model in 26664 iterations. The finding of the present research states that student having backlogs are less frequently opting for higher studies. [ABSTRACT FROM AUTHOR]

Stress is defined as the feeling of being overwhelmed or unable to cope with mental or emotional pressure. A variety of diseases or life situations might create stress. Stress causes a number of chemical changes in the body, resulting in fluctuating stress levels. Individuals' mental stress may result in issues such as depression, anxiety, and sleep disturbances. In India, more than 5 crore people are affected by the adverse effects of mental stress. As a result, a robust model based on machine learning approach to detect people's stress levels has been built to aid the community. By combining physiological data such as the Electrocardiogram (ECG) and Electro-Dermal Activity (EDA) with a Random Forest (RF) model to assess the level of stress in individuals, this research intends to produce an effective autonomous stress monitoring system. Precision, Classification accuracy, recall, confusion matrix, and f1-score assessment metrics are used to assess the RF model's performance. The algorithm's performance is evaluated using the SWELL Database. The proposed model is efficient to distinguish stressful working settings (interruptions and time pressure) from non-stressful ones with a high classification accuracy of 99.9% for ECG signals and 90.54 % for EDA signals. [ABSTRACT FROM AUTHOR]

In India, climate change has a huge impact on the agricultural sector. It directly affects crop yield, nutritional quality and productivity. In this paper, a model is devised to correctly predict the crop yield only from past data including factors like rainfall so that appropriate measures can be taken in the future. Machine Learning is one such tool which can be used to solve this issue. Random Forest Algorithm, XGBoost and K-Nearest Neighbor (KNN) Algorithm are the machine learning algorithms used for crop yield prediction in this paper and the one with higher accuracy can be used for future work. Further discussion includes how much impact each factor related to crop yield has on the predicted value so an ordinary farmer can plant and take care of his crops according to it and use this model for his daily work. In doing this, modernization of the agricultural sector is made possible and a boost is given to crop yield in India. [ABSTRACT FROM AUTHOR]

Agriculture is a field that plays an essential part in strengthening a country's economy, especially in agrarian countries like India, where agriculture and crop productivity play a large role in the economy. The research focuses on comparing machine learning and Deep learning algorithms in predicting total crop yield production. The parameters considered for the study are State name, District name, Year, Season, Crop, Area and Production. The dataset is resourced from the data.gov.in website. Random forest from Machine Learning and Sequential model from Deep learning are compared, and the performance metric considered for the study is R2 score. The objective is to assess how well the independent variable predicts the variance in the dependent variable. Random Forest algorithm achieved an R2 score of 0.89, whereas Deep Learning Sequential algorithm gave an R2 score of 0.29. [ABSTRACT FROM AUTHOR]

Currently, health management is a major concern all over the world. Particularly in India, due to population, the health issues are increasing rapidly. Diabetes has become a major issue that affects people all over India. With the advancement of Machine Learning approaches, many solutions have been developed to curb this serious issue. The goal of this paper is to design a model that can detect diabetes at preliminary stages with more accuracy. Therefore, various classification algorithms namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB) are used to solve this problem at earliest. Various performance metrics like accuracy, sensitivity, specificity, precision and recall are used for evaluation of the model. Evaluations are performed on PIMA Indian diabetes database, which is pulled from the Machine Learning cache. Results procured manifest that Random Forest outplay with the highest precision of 85.06%. The results are appropriately authenticated through a bar graph representation. [ABSTRACT FROM AUTHOR]