Anaconda Navigator Research Articles

Convolutional Neural Network Enactment Layer based Ambient Temperature Prediction of Electric Motor

As we know that Plan temperature evaluations apply to the most smoking spot inside the motor’s windings, not how much of that warm is exchanged to the motor’s surface. The warm exchange will change incredibly from engine to engine based on outline measure and mass, whether the outline is smooth or ribbed, whether open or completely encased, and other cooling variables. Indeed the productivity of the engine may have small impact on the surface temperature. An exact torque assess leads to more exact and satisfactory control of the engine, diminishing control misfortunes and in the long run heat build-up. In this venture, the machine learning strategies were utilized for the expectation of surrounding temperature of Electric engine. The forecast of ambient temperature of Electric engine are accomplished in four ways. With this context, we have utilized Electric motor temperature dataset extracted from UCI Machine Learning repository for predicting the ambient temperature prediction. The forecasting of ambient temperature are achieved in four ways. Firstly, the data set is preprocessed with Feature Scaling and missing values. Secondly, empirical feature examination is done and the relation of motor speed and ambient temperature of the motor is visualized. Thirdly, the fresh data set is fitted to all the regressors and the execution is dismembered before and after scaling. Fourth, the raw data set is subjected to Convolutional neural network Conv1D with various activation layers like Relu, Sigmoid, softmax, Softplus, Softsign, Tanh, Selu, Elu and exponential layers. The performance is analyzed with EVS, MAE, MSE, RScore and Step loss of the Convolutional neural network. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that the Conv1D-Softsign activation layer tends to reach the RScore of 99.842 with the step loss of 0.0001155.

Feature Selection Intent Machine Learning based Conjecturing Workout Burnt Calories

As we know that running is the victor for most calories burned per hour. Stationary bicycling, running, and swimming are fabulous choices as well. HIIT works out are too incredible for burning calories. After a HIIT workout, your body will proceed to burn calories for up to 24 hours. Forecasting the workout burnt calories still remains an open challenge as the changes in the environmental calamity and body health. The machine learning strategies can predict the burnt out calories for the course of exercise done by a body. With this background, we have utilized Exercise dataset extracted from UCI Machine Learning repository for predicting the workout burnt calories. The forecasting of burnt calories rate are achieved in four ways. Firstly, the data set is preprocessed with Feature Scaling and missing values. Secondly, exploratory feature examination is done and the scattering of target highlight is visualized. Thirdly, the raw data set is fitted to all the regressors and the execution is dissected before and after scaling. Fourth, the raw data set is subjected to feature selection axioms like Anova test, Correlated Feature, Variance Based and KBest Feature based methods and are fitted to all the regressors and the performance is analyzed before and after feature scaling. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that the Decision Tree and Gradient Boosting regressor tends to retain 99% before and after feature scaling for the Anova test, Correlated Feature, Variance Based and KBest Feature based methods.

Computational intelligence techniques for automatic detection of Wi-Fi attacks in wireless IoT networks

These days, number of smart products based on Internet-of-Things (IoT) has been increased. These products are unified via various wireless technologies like, Bluetooth, Z-wave, Wi-Fi, Zigbee, etc. While the need on the wireless networks has improved, the assaults against them throughout the time have expanded on top. In order to identify these assaults, an intrusion detection system (IDS) with a prominent precision and low identification time is required. In this work, a machine learning (ML) based wireless intrusion detection system (WIDS) for wireless networks to effectively identify assaults against them has been proposed. A ML prototype has been implemented to categorize the wireless network records into ordinary or one of the particular assault categories. The operation of an IDS is extensively enhanced when the attributes are more discriminative and delegate. Different attribute selection methods have been investigated to identify the best set of attributes for the WIDS. The proposed model is evaluated on aegean wireless intrusion dataset using various parameters like attack detection rate, detection time, precision, F-measure, etc. The experimental evaluation is carried out in the tools like, Weka, Rstudio and Anaconda Navigator Python. Finally, the experimental result shows the best performing ML algorithm with best set of reduced attributes.

Applying Government Schemes in Rural Sectors Prediction System for Evaluation of Data Science Algorithm

The administration dispatches different aggressive projects attempting to make the nation more prosperous, yet what they bomb is in fruitful execution and coming to recipients. The fundamental explanation for this issue is the absence of mindfulness among rustic individuals. This paper is to give an answer for this uninformed circumstance. Through this framework the rustic understudies will be instructed such that they can become acquainted with about what are the different plans that are outfitted by the administration and what are the plans they are qualified for. On the off chance that the country understudies came to know and get mindful of the apparent multitude of legislative plans gave by the Government of India for the government assistance of the provincial understudies, at that point their life would venture into next level. At first this framework will investigate the accessible government plans in the instructive for the government assistance of country understudies. Next, the understudy’s information ((i.e.) name, age, station, occupation, annualincome.etc) are accumulated. At that point; both the datasets are brought into the Anaconda Navigator. At that point, investigation and grouping dependent on networks (SC, ST, BC and MBC) of the understudies and the plans are performed. At that point utilizing the forecast calculations (Naïve Bayes, Random Forest and Support Vector Machine (SVM)) what are generally the plans the specific understudy is qualified for are anticipated. An investigation is made on the proficiency of the three calculations. The exactness of the three calculations is broke down and the effective calculation which creates the outcome with most elevated precision is at last used to play out the forecast of the plans that a specific understudy is qualified for. At long last, the anticipated plans anticipated utilizing the most elevated effective calculation among the three calculations will be gotten back to the understudies. Hence, through this undertaking the rustic understudies will come to think about different recipient plans gave by government and they can use those plans for the improvement of the country environmental factors.

КЛАСТЕРНЫЙ АНАЛИЗ РЕГИОНОВ РФ ДЛЯ ВЫЯВЛЕНИЯ ТЕРРИТОРИЙ – ДРАЙВЕРОВ УСТОЙЧИВОГО РАЗВИТИЯ: НАЛОГОВАЯ КОМПОНЕНТА

The relevance of tax clustering is due to the need for a competent scientifically grounded definition of territories that are drivers of economic growth. The aim of the study was to identify, on the basis of econometric methods, clusters of the regions of the Russian Federation by a set of indicators reflecting their tax status, tax administration, informatization of the tax environment. The Russian regions were grouped into clusters by a set of tax indicators based on official statistical data for 2018 using SPSS, Rstudio, Anaconda Navigator software. As a result of the anomalous values, five federal subjects were excluded from the analysis: Moscow, Sevastopol, Ingushetia, Khanty-Mansi and Yamalo-Nenets Autonomous Okrugs. Econometric analysis made it possible to conclude that there are three clusters of regions according to the analyzed parameters: 1) the least functionally proportional (7 regions), which have the lowest tax intensity of the gross regional product, the highest debt intensity of the gross regional product and the highest level of tax debt of the employed population, companies, and individual entrepreneurs; 2) medium functionally proportional (50 regions) with the lowest efficiency of tax administration, the highest coefficient of tax collection, the lowest level of taxation of the employed population and individual entrepreneurs (but not companies), the lowest level of tax debt of all analyzed subjects, and the lowest additional tax charges and sanctions for violation of tax legislation from tax audits, 3) the most comprehensively successful (22 regions), which are characterized by the highest tax intensity of the gross regional product and the highest level of tax revenues generated by the employed population, companies, and individual entrepreneurs. The regions of this cluster have the most effective taxation of value added and financial results of organizations. Among the regions of the third group, the leaders in terms of digital indicators are: Tyumen Oblast, Murmansk Oblast, Republic of Tatarstan, Leningrad Oblast. The study can develop in the following promising directions: 1) inclusion in the cluster analysis of indicators, not typical for the characteristics of the tax environment, that most fully reflect the influence of external diverse factors on the tax state of the regions; 2) extrapolation of the results to assess the tax status of the territories of other states; 3) the need to improve the tax clustering method based on artificial intelligence technology.

Using Deep Learning to Predict Scholarship Scheme Based on Student Details

The government launches various ambitious programs to make the country more prosperous, but they fail in successful implementation. The main reason behind this issue is the lack of awareness among rural people. This project is to provide a solution to this unaware situation. Through this system, rural students will get to know about what are the various schemes that are furnished by the government. Initially, this system will explore government schemes that are available for the welfare of rural students. Next, the student's datset ((i.e.) name, age, caste, occupation, annual income, etc.) are collected. Then both the datasets are imported into the Anaconda Navigator. Later analysis and classification are done based on communities (SC, ST, BC MBC, and DNC), Educational category (Pre-metric/Post-metric), Board of education (Government/Government-aided), Day scholar or hosteller, age of the students and the schemes are predicted.

Prediction of Student Performance System using Machine Learning Techniques

Educational organizations are unique and play the utmost significant role in the development of any country. In the Educational database, due to the enormous volume of data for predicting student's achievement becomes more complicated. To upgrade a student's performance and triumph is more efficient in a practical way using Educational Data Mining Techniques. Data Mining Techniques could deliver favor and brunt to educators and academic institutions. The student's data ((i.e.) Name,10th %,12th cut off, CGPA, No of arrears, etc.) are gathered. Then, the datasets are imported into the Anaconda Navigator. Then, analysis and classification based on attributes of the students and the schemes are performed. Then using the prediction algorithm Naïve Bayes what are all the features the particular student is eligible for are predicted as placed. The student's input that has disparate data about their past and present academics report and then apply the Naïve Bayes algorithm using Anaconda Navigator to search the student's achievement for placement. A proposed methodology based on a classification approach to finding an improved estimation method for predicting the placement for students. This project can find the association for academic achievement of each particular student and their placement achievement in campus selection.

Attribute Balanced Leveling with Ada Boost Regressor for Predicting Heart Disease using Machine Learning

The technological advancement can help the entire application field to predict the damage and to forecast the future target of the object. The wealth of the world is in the health of the people. So the technology must support the technologists in predicting the disease in advance. The machine learning is the emerging field which is used to forecast the existence of the heart disease through the values of the clinical parameters. With this view, we focus on predicting the customer churn for the banking application. This paper uses the customer churn bank modeling data set extracted from UCI Machine Learning Repository. The anaconda Navigator IDE along with Spyder is used for implementing the Python code. Our contribution is folded is folded in three ways. First, the data is processed to find the relationship between the elements of the dataset. Second, the data set is applied for Ada Boost regressors and the important elements are identified. Third, the dataset is applied to feature scaling and then fitted to kernel support vector machine, logistic regression classifier, Naive bayes classifier, random forest classifier, decision tree classifier and KNN classifier. Fourth, the dataset is dimensionality reduced with principal component analysis with five components and then applied to the previously mentioned classifiers. Fifth, the performance of the classifiers is analyzed with the indication metrics like precision, accuracy, recall and Fscore. The implementation is carried out with python code using Anaconda Navigator. Experimental results show that, the Naïve bayes classifier is more effective with the precision of 0.90 for dataset with random boost, feature scaled and PCA. Experimental results show that, the Naïve bayes classifier is more effective with the recall of 0.91 for dataset with random boost, feature scaled and PCA. Experimental results show that, the Naïve bayes classifier is more effective with the Fscore of 0.92 for dataset with random boost, feature scaled and PCA. Experimental results show, the Naïve bayes classifier is more effective with the accuracy of 91% without random boost, 93% with random boosting and 92% with principal component analysis.

Feature Scaled Element Balancing with Random Boosting for Heart Disease Prediction using Machine Learning

In the current scenario, the researchers are focusing towards health care project for the prediction of the disease and its type. In addition to the prediction, there exists a need to find the influencing parameter that directly related to the disease prediction. The analysis of the parameters needed to the prediction of the disease still remains a challenging issue. With this view, we focus on predicting the heart disease by applying the dataset with boosting the parameters of the dataset. The heart disease data set extracted from UCI Machine Learning Repository is used for implementation. The anaconda Navigator IDE along with Spyder is used for implementing the Python code. Our contribution is folded is folded in three ways. First, the data preprocessing is done and the attribute relationship is identified by the correlation values. Second, the data set is fitted to random boost regressor and the important features are identified. Third, the dataset is feature scaled reduced and then fitted to random forest classifier, decision tree classifier, Naïve bayes classifier, logistic regression classifier, kernel support vector machine and KNN classifier. Fourth, the dataset is reduced with principal component analysis with five components and then fitted to the above mentioned classifiers. Fifth, the performance of the classifiers is analyzed with the metrics like accuracy, recall, fscore and precision. Experimental results shows that, the Naïve bayes classifier is more effective with the precision, Recall and Fscore of 0.89 without random boost, 0.88 with random boosting and 0.90 with principal component analysis. Experimental results show, the Naïve bayes classifier is more effective with the accuracy of 89% without random boost, 90% with random boosting and 91% with principal component analysis.

Machine Learning Based Target Transformation using Regressor Classification for Heart Disease Envisage

In the modern scenario of technological growth, the life style of an individual varies with the economic status. The world population is prone towards chronic deadly diseases due to the variety of food habits. The usages of electronic equipments have raised the population to waste their quality time towards exercise. The lack of physical activity has symptoms towards bad quality of life. With this background information, this paper concentrates on predicting the type of heart disease by applying target transformation using various machine learning regression models. This paper uses the Heart disease data set extracted from UCI Machine Learning Repository. The anaconda Navigator IDE along with Spyder is used for implementing the Python code. Our contribution is folded is folded in three ways. First, the data segregation is done and it is preprocessed to extract the relationship and dependency of each parameters. Second, the dataset is subjected to process to identify the target distribution of classes in the dependent variable. Third, the dataset is fitted to the Ridge regressor, Huber regressor, SGD regressor and PerceptronCV regressor by applying with and without target transformation. Fourth, dataset is feature scaled and then fitted to the Ridge regressor, Huber regressor, SGD regressor and PerceptronCV regressor by applying with and without target transformation. Fifth, the performance analysis is done by analyzing the Mean Absolute Error and R2 Score. Experimental results show that, the Perceptron regressor CV has the effectiveness with the mean absolute error of 1.00 and R2 score of 0.04 for the heart disease prediction.

Postulation of Customer Retention in Banking Sector using Machine Learning and Principal Component

Recently, there is a rapid growth in technological improvement in banking sector. The entire world is using the banking service for managing their financial and property assets. As of now, all the technological advancements are applied to banking sector to facilitate the customers with proper operational excellence. In this view, the bank has complete responsibility in serving the people with their modern application to save their time and wealth. So the customer value analysis is needed for the bank to enrich the marketing growth and turnover of the bank. But still, the prediction of customer churn remains a challenging issue for the banking sector for analyzing the profit growth. With this view, we focus on predicting the customer churn for the banking application. This paper uses the churn modeling data set extracted from UCI Machine Learning Repository. The anaconda Navigator IDE along with Spyder is used for implementing the Python code. Our contribution is folded is folded in three ways. First, the data set is applied to various classifiers like Logistic Regression, KNN, Kernel SVM, Naive Bayes, Decision Tree, Random Forest to analyze the confusion matrix. The Performance analysis is done by comparing the metrics like Precision, Recall, FScore and Accuracy. Second the data set is subjected to dimensionality reduction method using Principal component Analysis and then fitted to the above mentioned classifiers and their performance analysis is done. Third, the performance analysis is done for the dataset by comparing the metrics with and without applying the dimensionality reduction. A Performance analysis is done with various classification algorithms and comparative study is done with the performance metric such as accuracy, precision, recall, and f-score. The implementation is carried out with python code using Anaconda Navigator. Experimental results shows that before applying dimensionality reduction PCA, the Random Forest classifier is found to be effective with the accuracy of 86%, Precision of 0.85, Recall of 0.86 and FScore of 0.84. Experimental results shows that after applying dimensionality reduction, the 2 component PCA with the kernel SVM classifier is found to be effective with the accuracy of 81%, Precision of 0.81, Recall of 0.81 and FScore of 0.74. compared to other classifiers.

Machine Learning Based Suspicion of Customer Detention in Banking with Diverse Solver Neighbors and Kernels

In the current moving technological business sector, the amount spent for attaching the new customer is highly expensive and time consuming process than adopting some methods to hold and retain the existing customers. So the business sector is in need to make a research on with holding the existing customers by using the current technology. The methods to make the retention of the existing customers with high reliablility are a challenging task. With this view, we focus on predicting the customer churn for the banking application. This paper uses the customer churn bank modeling data set extracted from UCI Machine Learning Repository. The anaconda Navigator IDE along with Spyder is used for implementing the Python code. Our contribution is folded is folded in three ways. First, the data preprocessing is done and the relationship between the attributes are identified. Second, the data set is reduced with the principal component analysis to form the 2 component feature reduced dataset. Third, the raw dataset and 2 component PCA reduced dataset is fitted to various solvers of logistic regression classifiers and the performance is analyzed with the confusion matrix. Fourth, the raw dataset and 2 component PCA reduced dataset is fitted to various neighboring algorithms of K-Nearest Neighbors classifiers and the performance is analyzed with the confusion matrix. Fifth, the raw dataset and 2 component PCA reduced dataset is fitted to various kernels of Support Vector Machine classifiers and the performance is analyzed with the confusion matrix. The implementation is carried out with python code using Anaconda Navigator. Experimental results shows that, the rbf kernel of Support vector machine classifier is effective with the accuracy of 85.8% before applying PCA and accuracy of 80.9% after applying PCA compared to other classifiers.

Interrogation of Sentiment Perusing With Hash Counting Vectorizer and Term Inverse Frequency Transformer Using Machine Learning Classification

With the fast growing technology, the business is moving towards increasing their profit by interpreting the customer satisfaction. The customer satisfaction can be analyzed in many ways. It is the responsibility of the business to analyze the customer satisfaction in order to improve their turnover and profit. With the current trend, the customers are giving their feedback through mobile and internet. With this overview, this paper attempts to analyze the sentiment of the customer feedback for the movie. The sentiment Analysis on movie Review dataset from the KAGGLE Machine learning repository is used for implementation. The type of sentiment classes is predicted through the following ways. Firstly, the sentiment count for each class is displayed and the top feature words for each sentiment class are also extracted from the dataset. Secondly, the dataset is sampled with counting vectorizer and then fitted with the classifiers like Logistic Regression Classifier, Linear SVM Classifier, Multinomial Naives Bayes Classifier, Gradient Boosting Classifer, Guassian Naive Bayes Classifier, Random Forest Classifier, Decision Tree Classifier and and Extra Tree Classifier. Thirdly, the dataset is sampled with Hashing vectorizer and then fitted with the above specified classifiers. Fourth, the dataset is sampled with TFIFD vectorizer and then fitted with the above specified classifiers. Fifth, the dataset is sampled with TFIFD Transformer and then fitted with the above specified classifiers. Sixth, the Performance analysis of classifiers is performed by analyzing the metrics like Precision, Recall, Fscore and Accuracy. The implementation is carried out using python code in Spyder Anaconda Navigator IP Console. Experimental results shows that the analysis of sentiment done by the random forest classifier is found to be more effective with the Accuracy of 89% for Counting vectorizer and TFIFD transformer, Accuracy of 87% for Hashing vectorizer and Accuracy of 88% for TFIFD vectorizer.

Disquisition of Sentiment Inquiry with Hashing and Counting Vectorizer using Machine Learning Classification

With the rapid growth in technology, analysis of feedback and reviews by the customers in companies and industries becomes a major challenge. The profit of the company mainly depends on the customer satisfaction. The view of the customer can be analyzed only through feedback. The review analysis can be utilized for the prediction of current sales and future sales of the company. With this overview, the paper aims in performing the sentiment analysis of the movie review. The Type of comment given by the customer is predicted and categorized into classes. The sentiment Analysis on movie Review dataset taken from the KAGGLE leading Dataset repository is used for implementation. The categorization of sentiment classes is achieved in five categories. Firstly, the target count for each sentiment is portrayed. The Resampling is done for equalizing the target sentiment count. Secondly, the extraction of sentiment feature words for each target is displayed and the data cleaning is done with Term Frequency Inverse document Frequency method. Thirdly, the resampled dataset is then fitted with the various classifiers like Multinomial Naives Bayes Classifier, Logistic Regression Classifier, KNearest Neighbors Classifier, Bernoulli Naives Bayes Classifier, Complement Naives Bayes Classifier, Nearest Centroid Classifer, Passive Aggressive Classifier, SGD Classifier, Ridge Classifier, Perceptron Classifier. Fourth, the feature extraction is done with Hashing Vectorizer and Counting Vectorizer. The vocabulary features are also displayed from the dataset. Fifth, the Performance analysis of clasifier is done with metrics like Accuracy, Recall, FScore and Precision. The implementation is carried out using python code in Spyder Anaconda Navigator IP Console. Experimental results shows that the sentiment prediction and classification done by Ridge classifier is found to be effective with Precision of 0.89, Recall of 0.88, FScore of 0.87 and Accuracy of 89%.

Exploration of Neighbor Kernels and Feature Estimators for Heart Disease Prediction using Machine Learning

In the growing era of technological world, the people are suffered with various diseases. The common disease faced by the population irrespective of the age is the heart disease. Though the world is blooming in technological aspects, the prediction and the identification of the heart disease still remains a challenging issue. Due to the deficiency of the availability of patient symptoms, the prediction of heart disease is a disputed charge. With this overview, we have used Heart Disease Prediction dataset extorted from UCI Machine Learning Repository for the analysis and comparison of various parameters in the classification algorithms. The parameter analysis of various classification algorithms of heart disease classes are done in five ways. Firstly, the analysis of dataset is done by exploiting the correlation matrix, feature importance analysis, Target distribution of the dataset and Disease probability based on the density distribution of age and sex. Secondly, the dataset is fitted to K-Nearest Neighbor classifier to analyze the performance for the various combinations of neighbors with and without PCA. Thirdly, the dataset is fitted to Support Vector classifier to analyze the performance for the various combinations of kernels with and without PCA. Fourth, the dataset is fitted to Decision Tree classifier to analyze the performance for the various combinations of features with and without PCA. Fifth, the dataset is fitted to Random Forest classifier to analyze the performance for the various levels of estimators with and without PCA. The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that for KNN classifier, the performance for 12 neighbours is found to be effective with 0.52 before applying PCA and 0.53 after applying PCA. For Support Vector classifier, the rbf kernel is found to be effective with the score of 0.519 with and without PCA. For Decision Tree classifier, before applying PCA, the score is 0.47 for 7 features and after applying PCA, the score is 0.49 for 4 features. For, Random Forest Classifier, before applying PCA, the score is 0.53 for 500 estimators and after applying PCA, the score is 0.52 for 500 estimators.

Ensembling Coalesce of Logistic Regression Classifier for Heart Disease Prediction using Machine Learning

In today’s modern world, the world population is affected with some kind of heart diseases. With the vast knowledge and advancement in applications, the analysis and the identification of the heart disease still remain as a challenging issue. Due to the lack of awareness in the availability of patient symptoms, the prediction of heart disease is a questionable task. The World Health Organization has released that 33% of population were died due to the attack of heart diseases. With this background, we have used Heart Disease Prediction dataset extracted from UCI Machine Learning Repository for analyzing and the prediction of heart disease by integrating the ensembling methods. The prediction of heart disease classes are achieved in four ways. Firstly, The important features are extracted for the various ensembling methods like Extra Trees Regressor, Ada boost regressor, Gradient booster regress, Random forest regressor and Ada boost classifier. Secondly, the highly importance features of each of the ensembling methods is filtered from the dataset and it is fitted to logistic regression classifier to analyze the performance. Thirdly, the same extracted important features of each of the ensembling methods are subjected to feature scaling and then fitted with logistic regression to analyze the performance. Fourth, the Performance analysis is done with the performance metric such as Mean Squared error (MSE), Mean Absolute error (MAE), R2 Score, Explained Variance Score (EVS) and Mean Squared Log Error (MSLE). The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that before applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.04, MAE of 0.07, R2 Score of 92%, EVS of 0.86 and MSLE of 0.16 as compared to other ensembling methods. Experimental results shows that after applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.09, MAE of 0.13, R2 Score of 91%, EVS of 0.93 and MSLE of 0.18 as compared to other ensembling methods.

Exploration of Multiple Linear Regression with Ensembling Schemes for Roof Fall Assessment using Machine Learning

Roof fall of the building is the major threat to the society as it results in severe damages to the life of the people. Recently, engineers are focusing on the prediction of roof fall of the building in order to avoid the damage to the environment and people. Early prediction of Roof fall is the social responsibility of the engineers towards existence of health and wealth of the nation. This paper attempts to identify the essential attributes of the Roof fall dataset that are taken from the UCI Machine learning repository for predicting the existence of roof fall. In this paper, the important features are extorted from the various ensembling methods like Gradient Boosting Regressor, Random Forest Regressor, AdaBoost Regressor and Extra Trees Regressor. The extracted feature importance of each of the ensembling methods is then fitted with multiple linear regression to analyze the performance. The same extracted feature importance of each of the ensembling methods are subjected to feature scaling and then fitted with multiple linear regression to analyze the performance. The Performance analysis is done with the performance parameters such as Mean Squared Log Error (MSLE), Mean Absolute error (MAE), R2 Score, Mean Squared error (MSE) and Explained Variance Score (EVS). The execution is carried out using python code in Spyder Anaconda Navigator IP Console. Experimental results shows that before feature scaling, Extra Tree Regressor is found to be effective with the MSE of 0.06, MAE of 0.07, R2 Score of 87%, EVS of 0.89 and MSLE of 0.02 as compared to other ensembling methods. In the same way, after applying feature scaling, the feature importance extracted from the Extra Tree Regressor is found to be effective with the MSE of 0.04, MAE of 0.03, R2 Score of 96%, EVS of 0.9 and MSLE of 0.01 as compared to other ensembling methods.

Heart Disease Prediction and Performance Assessment through Attribute Element Diminution using Machine Learning

In today’s modern world, the human beings are affected with heart disease irrespective of the age. With the advancement of technological growth, predicting the availability of Heart diseases still remains a challenging issue. The difficulty of predicting the heart disease prevails due to the lack of availability of the symptoms. According to World Health Organization, 33% of population died due to heart diseases. For this, the diagnosis of heart diseases is made by complex combination of clinical data. With this overview, we have used Heart Disease Prediction dataset extracted from UCI Machine Learning Repository for predicting the level of heart disease. The prediction of heart disease classes are achieved in four ways. Firstly, the data set is preprocessed with Feature Scaling and Missing Values. Secondly, the raw data set is fitted to classifiers like logistic regression, KNN classifier, Support Vector Machine, Kernel Support Vector Machine, Naive Bayes, Random Forest and Decision Tree classifiers. Third, the raw data set is subjected to dimensionality reduction using Principal Component Analysis to project the dataset with important components. The dimensionality PCA reduced data set is fitted to the above-mentioned classifiers. Fourth, the performance comparison of raw data set and PCA reduced data set is done by analyzing the performance metrics like Precision, Recall, Accuracy and F-score. The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that Random forest is found to be effective with the accuracy of 89% without applying PCA, 85% with five component PCA and 86% with seven component PCA.

Composite Model Fabrication of Classification with Transformed Target Regressor for Customer Segmentation using Machine Learning

In Current internet world, the customers prefer to buy the products through online rather than spending their time on show rooms. The online customers of wine increases day by day due to the availability of high brands in online sellers. So the customers buy the wine products based on the product description and the satisfaction of other customers those who have bought before. This makes the industries to focus on machine learning that concentrates on target transformation of the dependent variable. This paper endeavor to forecast the customer segmentation for the wine data set extracted from UCI Machine learning repository. The raw wine data set is subjected to target transformation for various classifiers like Huber Regressor, SGD Regressor, RidgeCV Regression, Logistic RegressionCV and Passive Aggressive Regressor. The performance of the various classifiers is analyzed with and without target transformation using the metrics like Mean Absolute Error and R2 Score. The implementation is done in Anaconda Navigator with Python. Experimental results shows that after applying target transformation RidgeCV Regression is found to be effective with the R2 Score of 82% and Mean Absolute Error of 0.0 compared to other classifiers.

Twitter analysis of founders of top 25 Indian startups

With the recent growth in social media dominance over the internet, many people have turned to these media to share their personal views, express their opinions and interact socially. This paper examines the social media content of Indian Chief Executive Officers who are the founders or co-founders of the top 25 Indian startups. The active involvement of Chief Executive Officers in social media influences the business effectiveness, performance and market legitimacy of the business. Social media has now become a crucial strategic tool from creating a buzz among businesses to engaging public in a new direction of thoughts. The paper has taken into consideration about 165,987 tweets from the top startups and their cofounders. The tools used for tweets extraction and analysis are Anaconda Navigator and TIBCO Spotfire. The tweets seem to cover various aspects of emotions ranging from business sector to personal feelings, political views and societal concerns.