Analysis And Prediction Projects Using Machine Learning And Deep Learning With Python
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Artificial Intelligence With Python
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Author : Prateek Joshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-01-27
Artificial Intelligence With Python written by Prateek Joshi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-27 with Computers categories.
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
Airline Passenger Satisfaction Analysis And Prediction Using Machine Learning And Deep Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-08-08
Airline Passenger Satisfaction Analysis And Prediction Using Machine Learning And Deep Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-08 with Computers categories.
In the project "Airline Passenger Satisfaction Analysis and Prediction Using Machine Learning and Deep Learning with Python," the aim was to analyze and predict passenger satisfaction in the airline industry. The project began with an extensive data exploration phase, wherein the dataset containing various features related to passenger experiences was thoroughly examined. The dataset was then preprocessed, ensuring data cleanliness and preparing it for further analysis. One of the initial steps involved understanding the distribution of categorized features within the dataset. By visualizing the distribution of these features, insights were gained into the prevalence of different categories, providing a preliminary understanding of passenger preferences and experiences. For the prediction aspect, machine learning models were employed, and a Grid Search approach was implemented to fine-tune hyperparameters and optimize model performance. This process allowed the identification of the best-performing model configuration, enhancing the accuracy of passenger satisfaction predictions. The models used are Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting. Going beyond traditional machine learning, a Deep Learning approach was introduced using an Artificial Neural Network (ANN). This model, designed to capture intricate patterns and relationships within the data, showcased the potential of deep learning for improving predictive accuracy. The evaluation of both machine learning and deep learning models was centered around key metrics. The accuracy score was a primary indicator of model performance, reflecting the ratio of correctly predicted passenger satisfaction outcomes. Additionally, the Classification Report provided a comprehensive overview of precision, recall, and F1-score for each category, shedding light on the model's ability to classify passenger satisfaction levels accurately. Visualizing the results played a pivotal role in the project. The plotted Training and Validation Accuracy and Loss graphs offered insights into the convergence and generalization capabilities of the models. These visualizations helped in understanding potential overfitting or underfitting issues and guided the fine-tuning process. To assess the models' predictive performance, a Confusion Matrix was constructed. This matrix presented a clear breakdown of correct and incorrect predictions, facilitating an understanding of where the model excelled and where it struggled. Furthermore, scatter plots were utilized to visually compare the predicted values against the actual true values, offering a tangible representation of the models' effectiveness. Throughout the project, rigorous data preprocessing and feature engineering were integral to improving model accuracy. Features were appropriately scaled, and categorical variables were transformed using techniques like one-hot encoding, enabling models to efficiently learn from the data. The project also focused on the interpretability of the models, enabling stakeholders to comprehend the factors influencing passenger satisfaction predictions. This interpretability was essential for making informed business decisions based on the model insights. In conclusion, the project showcased a comprehensive approach to analyzing and predicting airline passenger satisfaction. Through meticulous data exploration, feature distribution analysis, machine learning model selection, hyperparameter tuning, and deep learning implementation, the project provided valuable insights for the airline industry. By utilizing a combination of machine learning and deep learning techniques, the project demonstrated a holistic approach to understanding and enhancing passenger experiences and satisfaction levels.
Deep Learning With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2016-05-13
Deep Learning With Python written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-13 with Computers categories.
Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.
Python Machine Learning
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Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-09-23
Python Machine Learning written by Sebastian Raschka and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-23 with Computers categories.
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Credit Card Churning Customer Analysis And Prediction Using Machine Learning And Deep Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-18
Credit Card Churning Customer Analysis And Prediction Using Machine Learning And Deep Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-18 with Computers categories.
The project "Credit Card Churning Customer Analysis and Prediction Using Machine Learning and Deep Learning with Python" involved a comprehensive analysis and prediction task focused on understanding customer attrition in a credit card churning scenario. The objective was to explore a dataset, visualize the distribution of features, and predict the attrition flag using both machine learning and artificial neural network (ANN) techniques. The project began by loading the dataset containing information about credit card customers, including various features such as customer demographics, transaction details, and account attributes. The dataset was then explored to gain a better understanding of its structure and contents. This included checking the number of records, identifying the available features, and inspecting the data types. To gain insights into the data, exploratory data analysis (EDA) techniques were employed. This involved examining the distribution of different features, identifying any missing values, and understanding the relationships between variables. Visualizations were created to represent the distribution of features. These visualizations helped identify any patterns, outliers, or potential correlations in the data. The target variable for prediction was the attrition flag, which indicated whether a customer had churned or not. The dataset was split into input features (X) and the target variable (y) accordingly. Machine learning algorithms were then applied to predict the attrition flag. Various classifiers such as Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (NN), Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, were utilized. These models were trained using the training dataset and evaluated using appropriate performance metrics. Model evaluation involved measuring the accuracy, precision, recall, and F1-score of each classifier. These metrics provided insights into how well the models performed in predicting customer attrition. Additionally, a confusion matrix was created to analyze the true positive, true negative, false positive, and false negative predictions. This matrix allowed for a deeper understanding of the classifier's performance and potential areas for improvement. Next, a deep learning approach using an artificial neural network (ANN) was employed for attrition flag prediction. The dataset was preprocessed, including features normalization, one-hot encoding of categorical variables, and splitting into training and testing sets. The ANN model architecture was defined, consisting of an input layer, one or more hidden layers, and an output layer. The number of nodes and activation functions for each layer were determined based on experimentation and best practices. The ANN model was compiled by specifying the loss function, optimizer, and evaluation metrics. Common choices for binary classification problems include binary cross-entropy loss and the Adam optimizer. The model was then trained using the training dataset. The training process involved feeding the input features and target variable through the network, updating the weights and biases using backpropagation, and repeating this process for multiple epochs. During training, the model's performance on both the training and validation sets was monitored. This allowed for the detection of overfitting or underfitting and the adjustment of hyperparameters, such as the learning rate or the number of hidden layers, if necessary. The accuracy and loss values were plotted over the epochs to visualize the training and validation performance of the ANN. These plots provided insights into the model's convergence and potential areas for improvement. After training, the model was used to make predictions on the test dataset. A threshold of 0.5 was applied to the predicted probabilities to classify the predictions as either churned or not churned customers. The accuracy score was calculated by comparing the predicted labels with the true labels from the test dataset. Additionally, a classification report was generated, including metrics such as precision, recall, and F1-score for both churned and not churned customers. To further evaluate the model's performance, a confusion matrix was created. This matrix visualized the true positive, true negative, false positive, and false negative predictions, allowing for a more detailed analysis of the model's predictive capabilities. Finally, a custom function was utilized to create a plot comparing the predicted values to the true values for the attrition flag. This plot visualized the accuracy of the model and provided a clear understanding of how well the predictions aligned with the actual values. Through this comprehensive analysis and prediction process, valuable insights were gained regarding customer attrition in credit card churning scenarios. The machine learning and ANN models provided predictions and performance metrics that can be used for decision-making and developing strategies to mitigate attrition. Overall, this project demonstrated the power of machine learning and deep learning techniques in understanding and predicting customer behavior. By leveraging the available data, it was possible to uncover patterns, make accurate predictions, and guide business decisions aimed at retaining customers and reducing attrition in credit card churning scenarios.
Master Machine Learning Algorithms
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2016-03-04
Master Machine Learning Algorithms written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-04 with Computers categories.
You must understand the algorithms to get good (and be recognized as being good) at machine learning. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step.
Company Bankruptcy Analysis And Prediction Using Machine Learning With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-08-25
Company Bankruptcy Analysis And Prediction Using Machine Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-25 with Computers categories.
In this comprehensive project titled "Company Bankruptcy Analysis and Prediction Using Machine Learning with Python GUI," we embarked on a journey to explore, analyze, and predict the bankruptcy status of companies. Our project began with an exploration of the dataset, which involved importing it using Pandas and refining it by removing leading spaces and replacing spaces with underscores in column names to ensure consistency. To grasp the dataset's characteristics, we delved into categorized features' distributions, allowing us to understand the underlying patterns within the data. This step helped us gain insights into the distribution of attributes across different classes, aiding in feature selection and engineering. Moving on to the heart of our project, the prediction of company bankruptcy, we employed various machine learning models. Utilizing grid search, we performed hyperparameter tuning to optimize model performance. Our model arsenal included Logistic Regression, K-Nearest Neighbors, Support Vector, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), which were evaluated using accuracy, precision, recall, and F1-score. Transitioning to deep learning, we implemented an Artificial Neural Network (ANN) model. This involved constructing a feed-forward neural network with hidden layers, dropouts, and activation functions. We evaluated the ANN using accuracy, precision, recall, and F1-score, gaining a comprehensive understanding of its classification performance. Our journey into deep learning continued with the implementation of Long Short-Term Memory (LSTM) networks, which are well-suited for sequence data. We structured the LSTM model with multiple layers and dropouts, evaluating its performance using metrics like accuracy, precision, recall, and F1-score. This marked a pivotal step in predicting company bankruptcy. Furthermore, we explored Feed-Forward Neural Networks (FNN) for prediction. Constructing a multi-layered architecture with varied dropouts and activation functions, we assessed its classification capabilities using metrics similar to previous models. Incorporating Recurrent Neural Networks (RNN) added another dimension to our analysis. Building an RNN model with sequential data, we examined its accuracy, precision, recall, and F1-score, highlighting its ability to capture sequential patterns in bankruptcy data. To comprehensively evaluate our models, we employed a range of metrics including precision, recall, F1-score, and accuracy. These metrics enabled us to gauge not only the overall model performance but also its capability to correctly predict bankrupt and non-bankrupt cases. Our project also extended into creating a Python GUI using PyQt. This graphical interface facilitated user interaction, allowing them to input data for prediction and view the outcomes through an intuitive interface. This GUI enhanced accessibility and usability, making it easier for users to engage with our models. In conclusion, our journey through the "Company Bankruptcy Analysis and Prediction Using Machine Learning with Python GUI" project encompassed data exploration, categorized features distribution analysis, model selection, performance evaluation using diverse metrics, and the creation of an interactive GUI. This endeavor combined analytical rigor, machine learning expertise, and user-centric design to provide a comprehensive solution for predicting company bankruptcy.
Stroke Analysis And Prediction Using Scikit Learn Keras And Tensorflow With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-15
Stroke Analysis And Prediction Using Scikit Learn Keras And Tensorflow With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-15 with Computers categories.
In this project, we will perform an analysis and prediction task on stroke data using machine learning and deep learning techniques. The entire process will be implemented with Python GUI for a user-friendly experience. We start by exploring the stroke dataset, which contains information about various factors related to individuals and their likelihood of experiencing a stroke. We load the dataset and examine its structure, features, and statistical summary. Next, we preprocess the data to ensure its suitability for training machine learning models. This involves handling missing values, encoding categorical variables, and scaling numerical features. We utilize techniques such as data imputation and label encoding. To gain insights from the data, we visualize its distribution and relationships between variables. We create plots such as histograms, scatter plots, and correlation matrices to understand the patterns and correlations in the data. To improve model performance and reduce dimensionality, we select the most relevant features for prediction. We employ techniques such as correlation analysis, feature importance ranking, and domain knowledge to identify the key predictors of stroke. Before training our models, we split the dataset into training and testing subsets. The training set will be used to train the models, while the testing set will evaluate their performance on unseen data. We construct several machine learning models to predict stroke. These models include Support Vector, Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, Light Gradient Boosting, Naive Bayes, Adaboost, and XGBoost. Each model is built and trained using the training dataset. We train each model on the training dataset and evaluate its performance using appropriate metrics such as accuracy, precision, recall, and F1-score. This helps us assess how well the models can predict stroke based on the given features. To optimize the models' performance, we perform hyperparameter tuning using techniques like grid search or randomized search. This involves systematically exploring different combinations of hyperparameters to find the best configuration for each model. After training and tuning the models, we save them to disk using joblib. This allows us to reuse the trained models for future predictions without having to train them again. With the models trained and saved, we move on to implementing the Python GUI. We utilize PyQt libraries to create an interactive graphical user interface that provides a seamless user experience. The GUI consists of various components such as buttons, checkboxes, input fields, and plots. These components allow users to interact with the application, select prediction models, and visualize the results. In addition to the machine learning models, we also implement an ANN using TensorFlow. The ANN is trained on the preprocessed dataset, and its architecture consists of a dense layer with a sigmoid activation function. We train the ANN on the training dataset, monitoring its performance using metrics like loss and accuracy. We visualize the training progress by plotting the loss and accuracy curves over epochs. Once the ANN is trained, we save the model to disk using the h5 format. This allows us to load the trained ANN for future predictions. In the GUI, users have the option to choose the ANN as the prediction model. When selected, the ANN model is loaded from disk, and predictions are made on the testing dataset. The predicted labels are compared with the true labels for evaluation. To assess the accuracy of the ANN predictions, we calculate various evaluation metrics such as accuracy score, precision, recall, and classification report. These metrics provide insights into the ANN's performance in predicting stroke. We create plots to visualize the results of the ANN predictions. These plots include a comparison of the true values and predicted values, as well as a confusion matrix to analyze the classification accuracy. The training history of the ANN, including the loss and accuracy curves over epochs, is plotted and displayed in the GUI. This allows users to understand how the model's performance improved during training. In summary, this project covers the analysis and prediction of stroke using machine learning and deep learning models. It encompasses data exploration, preprocessing, model training, hyperparameter tuning, GUI implementation, ANN training, and prediction visualization. The Python GUI enhances the user experience by providing an interactive and intuitive platform for exploring and predicting stroke based on various features.
Analysis And Prediction Projects Using Machine Learning And Deep Learning With Python
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-02-17
Analysis And Prediction Projects Using Machine Learning And Deep Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-17 with Computers categories.
PROJECT 1: DEFAULT LOAN PREDICTION BASED ON CUSTOMER BEHAVIOR Using Machine Learning and Deep Learning with Python In finance, default is failure to meet the legal obligations (or conditions) of a loan, for example when a home buyer fails to make a mortgage payment, or when a corporation or government fails to pay a bond which has reached maturity. A national or sovereign default is the failure or refusal of a government to repay its national debt. The dataset used in this project belongs to a Hackathon organized by "Univ.AI". All values were provided at the time of the loan application. Following are the features in the dataset: Income, Age, Experience, Married/Single, House_Ownership, Car_Ownership, Profession, CITY, STATE, CURRENT_JOB_YRS, CURRENT_HOUSE_YRS, and Risk_Flag. The Risk_Flag indicates whether there has been a default in the past or not. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 2: AIRLINE PASSENGER SATISFACTION Analysis and Prediction Using Machine Learning and Deep Learning with Python The dataset used in this project contains an airline passenger satisfaction survey. In this case, you will determine what factors are highly correlated to a satisfied (or dissatisfied) passenger and predict passenger satisfaction. Below are the features in the dataset: Gender: Gender of the passengers (Female, Male); Customer Type: The customer type (Loyal customer, disloyal customer); Age: The actual age of the passengers; Type of Travel: Purpose of the flight of the passengers (Personal Travel, Business Travel); Class: Travel class in the plane of the passengers (Business, Eco, Eco Plus); Flight distance: The flight distance of this journey; Inflight wifi service: Satisfaction level of the inflight wifi service (0:Not Applicable;1-5); Departure/Arrival time convenient: Satisfaction level of Departure/Arrival time convenient; Ease of Online booking: Satisfaction level of online booking; Gate location: Satisfaction level of Gate location; Food and drink: Satisfaction level of Food and drink; Online boarding: Satisfaction level of online boarding; Seat comfort: Satisfaction level of Seat comfort; Inflight entertainment: Satisfaction level of inflight entertainment; On-board service: Satisfaction level of On-board service; Leg room service: Satisfaction level of Leg room service; Baggage handling: Satisfaction level of baggage handling; Check-in service: Satisfaction level of Check-in service; Inflight service: Satisfaction level of inflight service; Cleanliness: Satisfaction level of Cleanliness; Departure Delay in Minutes: Minutes delayed when departure; Arrival Delay in Minutes: Minutes delayed when Arrival; and Satisfaction: Airline satisfaction level (Satisfaction, neutral or dissatisfaction) The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 3: CREDIT CARD CHURNING CUSTOMER ANALYSIS AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON The dataset used in this project consists of more than 10,000 customers mentioning their age, salary, marital_status, credit card limit, credit card category, etc. There are 20 features in the dataset. In the dataset, there are only 16.07% of customers who have churned. Thus, it's a bit difficult to train our model to predict churning customers. Following are the features in the dataset: 'Attrition_Flag', 'Customer_Age', 'Gender', 'Dependent_count', 'Education_Level', 'Marital_Status', 'Income_Category', 'Card_Category', 'Months_on_book', 'Total_Relationship_Count', 'Months_Inactive_12_mon', 'Contacts_Count_12_mon', 'Credit_Limit', 'Total_Revolving_Bal', 'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1', 'Total_Trans_Amt', 'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', and 'Avg_Utilization_Ratio',. The target variable is 'Attrition_Flag'. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 4: MARKETING ANALYSIS AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON This data set was provided to students for their final project in order to test their statistical analysis skills as part of a MSc. in Business Analytics. It can be utilized for EDA, Statistical Analysis, and Visualizations. Following are the features in the dataset: ID = Customer's unique identifier; Year_Birth = Customer's birth year; Education = Customer's education level; Marital_Status = Customer's marital status; Income = Customer's yearly household income; Kidhome = Number of children in customer's household; Teenhome = Number of teenagers in customer's household; Dt_Customer = Date of customer's enrollment with the company; Recency = Number of days since customer's last purchase; MntWines = Amount spent on wine in the last 2 years; MntFruits = Amount spent on fruits in the last 2 years; MntMeatProducts = Amount spent on meat in the last 2 years; MntFishProducts = Amount spent on fish in the last 2 years; MntSweetProducts = Amount spent on sweets in the last 2 years; MntGoldProds = Amount spent on gold in the last 2 years; NumDealsPurchases = Number of purchases made with a discount; NumWebPurchases = Number of purchases made through the company's web site; NumCatalogPurchases = Number of purchases made using a catalogue; NumStorePurchases = Number of purchases made directly in stores; NumWebVisitsMonth = Number of visits to company's web site in the last month; AcceptedCmp3 = 1 if customer accepted the offer in the 3rd campaign, 0 otherwise; AcceptedCmp4 = 1 if customer accepted the offer in the 4th campaign, 0 otherwise; AcceptedCmp5 = 1 if customer accepted the offer in the 5th campaign, 0 otherwise; AcceptedCmp1 = 1 if customer accepted the offer in the 1st campaign, 0 otherwise; AcceptedCmp2 = 1 if customer accepted the offer in the 2nd campaign, 0 otherwise; Response = 1 if customer accepted the offer in the last campaign, 0 otherwise; Complain = 1 if customer complained in the last 2 years, 0 otherwise; and Country = Customer's location. The machine and deep learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 5: METEOROLOGICAL DATA ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON Meteorological phenomena are described and quantified by the variables of Earth's atmosphere: temperature, air pressure, water vapour, mass flow, and the variations and interactions of these variables, and how they change over time. Different spatial scales are used to describe and predict weather on local, regional, and global levels. The dataset used in this project consists of meteorological data with 96453 total number of data points and with 11 attributes/columns. Following are the columns in the dataset: Formatted Date; Summary; Precip Type; Temperature (C); Apparent Temperature (C); Humidity; Wind Speed (km/h); Wind Bearing (degrees); Visibility (km); Pressure (millibars); and Daily Summary. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.
Python Artificial Intelligence Projects For Beginners
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Author : Dr. Joshua Eckroth
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-07-31
Python Artificial Intelligence Projects For Beginners written by Dr. Joshua Eckroth and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-31 with Computers categories.
Build smart applications by implementing real-world artificial intelligence projects Key Features Explore a variety of AI projects with Python Get well-versed with different types of neural networks and popular deep learning algorithms Leverage popular Python deep learning libraries for your AI projects Book Description Artificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence. This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library. By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progress What you will learn Build a prediction model using decision trees and random forest Use neural networks, decision trees, and random forests for classification Detect YouTube comment spam with a bag-of-words and random forests Identify handwritten mathematical symbols with convolutional neural networks Revise the bird species identifier to use images Learn to detect positive and negative sentiment in user reviews Who this book is for Python Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with code