Prediction, Prevention, and Analysis of Customer Churn in the Telecommunications Industry completion of this project for the client include: Title: Bike Sharing Application There are many industries in which an image recognition system can be used, including: The telecommunications industry is one of the sectors that is proliferating the fastest in the world. The indicators clearly reveal that increased competition encourages the customer to opt for low-cost options. This, combined with a lack of personal contact from larger companies, can lead to disloyalty and persuade a customer to switch to another provider, known as “customer churn”. Therefore, it has become imperative for mobile operators to move away from their rapid acquisition strategies and focus on helping maintain customers and improve margins of the existing customer base. Say no to plagiarism. Get a Tailored Essay on “Why Violent Video Games Should Not Be Banned”? Get an Original Essay To address this operational challenge, it is essential to use available customer behavior data to identify unique and actionable factors that influence customer loyalty. This project provided insights into the relationships between churn and different customer behavior attributes such as: seniority, contract, payment methods, monthly fees, etc. After extracting this valuable information, a model is built using logistic regression to predict whether a customer will churn. The model returned an AIC (Akaike Information Criterion) of 4,158.2, with a VIF (Variance Inflation Factor) less than 2. Since various statistical tests were performed to evaluate the predictions and avoid overfitting, the model has been shown to be accurate in predicting churn. of customers. Identifying the churn risk score for each customer and identifying customer behaviors that anticipate churn are essential foundations for targeted proactive retention of customers who switch to another provider. Enable a tailor-made marketing action for each customer. Discover associations between a retailer's products. Transactional data can be used to develop models that predict which products a user will purchase again, try for the first time, or subsequently add to their cart during a session. Market basket analysis is a modeling technique based on the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. Market basket analysis can be used as a recommendation system, using company data MBA can suggest the next best product a customer is likely to purchase. This can also be used to give the user discounts on products that the user is unlikely to purchase. This problem involves predicting which previously purchased products will be in the user's next order using anonymized data from the customer's orders over time. Our model can predict whether a customer will repurchase a certain product with 87% accuracy. Market basket analysis has proven to be essential for maintaining inventory, creating promotional strategies such as cross-selling. Market basket analysis can alsobe used to make decisions about in-store and online product placement. Recommender systems have become ubiquitous in our daily lives, with uses including, but not limited to, e-commerce, entertainment, research, and academia. A recommender system is an information filtering system that predicts a user's preferences and makes suggestions based on those preferences. Popular examples include YouTube, Netflix, and Amazon, all of which offer personalized recommendations to users. These systems can collect information about a user's behavior, using that information to improve their suggestions in the future. Content-based filtering is used by recommending items based on the interests contained in a user profile and the content of the items. Alternatively, collaborativefiltering groups similar users and uses the information about the group to make recommendations to the user. This project involved the creation of a collaborative movie rating filter recommendation system to model data and predict movie ratings that have not yet been assigned by users. . At first, we extracted data from the customer database and created a dictionary for each user and the movies they watched along with their respective ratings. An artificial neural network was used, and using data to test the accuracy of the model, an RMSE value of 0.96 was found, proving that there is no overfitting in the model. Parking Solutions A parking solution system is a service in which the number of empty and occupied spaces is made available to users of a parking area in real time. In this project, we used machine learning to develop an image-based classification system to predict whether a parking space is occupied or empty, with the aim of providing this information to the end user in a web application . An Inception V3 model is trained and developed from parking lot images. Using an existing camera system, we can obtain real-time images of a parking lot, which Inception V3 model can classify the lot as occupied or vacant, and frequently update this information in the database. This information can provide extensive parking details to end users through web applications. Parking solution systems are essential for hotels, leisure and retail areas because they obtain important information on customer capacity and peak times, which can facilitate many other business decisions. By adopting this data-driven solution, a parking lot can increase its efficiency and optimize its usage. Businesses can use our service to give their customers valuable information about how busy the area is and how convenient it is for them to visit. Email Classification Spam and fraudulent emails are unsolicited messages sent to consumers, often for marketing purposes or fraudulent activities. . These messages are time-consuming, annoying and, above all, can pose a threat to the recipient. Although the "Junk" folder is used to filter these messages, more advanced spamming techniques have recently been used. As a result, our spam folders often ignore these spam messages and put recipients at risk again. Our team used supervised classification to create a spam filter, to automatically classify an email based on itscontained in “Spam” or “Ham”. Using natural language processing and Naïve Bayes classification, a fast and highly scalable method, this machine learning. Not having an accurate spam filter can put you or your business at risk of harmful viruses and malware. A good spam filter won't let important emails slip through either. It is important that every employee in a company works efficiently. By allowing spam to flow into the inbox, you will spend considerable time searching for important emails and deleting all spam. This may seem trivial in everyday life, but over months and years, hours and days can be lost. Title: Web Traffic Analysis and Forecasting Web traffic is the interaction between users and a website; how many users visit a website, which pages a user clicks on, and the time spent on a page, just to name a few. By analyzing this traffic, we can find trends, or the most and least popular pages on a website. Time series analysis and forecasting can be done on temporal web traffic data, and in this project, we have predicted the number of people who will visit. web pages in the next 61 days. SMAPE (Symmetric Mean Absolute Percent Error) was chosen as a metric to evaluate the performance of our model and was found to be 0.13. Monitoring web traffic is vital to the success of any business. This helps to understand which products and services are popular on the page. Web traffic monitoring is only effective in conjunction with analytics and forecasting, to make informed decisions to optimize a website. Understanding as well as business information helps move the panels on the web page containing the products and services to provide customers with more ease of use when they find the product/service they want. For the web page, it will increase customer usage as well as business performance. Big Data is a term used to define a very large amount of all variable information recorded by a business. Data can be considered “big” depending. Usual data processing is often inadequate and expensive for Big Data, as it requires high computing power, constant maintenance, and regular scaling. This project was designed to create a pipeline to ingest an organization's data using Big Data technologies. After transferring data from the local system to an Amazon Web Services (AWS) EC2 instance, there are two ways to go about taming big data. The first is to import the data into SQLite using Python, then use the SQLite browser, checking the schema and exporting to SQL. The SQL file can then be opened in python to run a command that inserts the data into AWS RDS. The second method follows a similar structure, but uses Hadoop HDFS and Sqoop to insert the data into AWS RDS. Using HDFS and Sqoop is the fastest method of operation, but if the data contains unusual information, Sqoop is likely to generate errors. Using SQLite and Python will minimize these errors, but with a slightly slower calculation time. The benefits of completing this project for the client include: The flexibility of being able to access the data from anywhere The system will not become slow when processing the data Since the data is not in a local system, in In the event of a failure, the data is safe. Flexible System Scaling Title: Bike Sharing Application In.

Essay / Prediction, Prevention, and Analysis of Customer Churn in the Telecommunications Industry completion of this project for the client include: Title: Bike Sharing Application There are many industries in which an image recognition system can be used, including: The telecommunications industry is one of the sectors that is proliferating the fastest in the world. The indicators clearly reveal that increased competition encourages the customer to opt for low-cost options. This, combined with a lack of personal contact from larger companies, can lead to disloyalty and persuade a customer to switch to another provider, known as “customer churn”. Therefore, it has become imperative for mobile operators to move away from their rapid acquisition strategies and focus on helping maintain customers and improve margins of the existing customer base. Say no to plagiarism. Get a Tailored Essay on “Why Violent Video Games Should Not Be Banned”? Get an Original Essay To address this operational challenge, it is essential to use available customer behavior data to identify unique and actionable factors that influence customer loyalty. This project provided insights into the relationships between churn and different customer behavior attributes such as: seniority, contract, payment methods, monthly fees, etc. After extracting this valuable information, a model is built using logistic regression to predict whether a customer will churn. The model returned an AIC (Akaike Information Criterion) of 4,158.2, with a VIF (Variance Inflation Factor) less than 2. Since various statistical tests were performed to evaluate the predictions and avoid overfitting, the model has been shown to be accurate in predicting churn. of customers. Identifying the churn risk score for each customer and identifying customer behaviors that anticipate churn are essential foundations for targeted proactive retention of customers who switch to another provider. Enable a tailor-made marketing action for each customer. Discover associations between a retailer's products. Transactional data can be used to develop models that predict which products a user will purchase again, try for the first time, or subsequently add to their cart during a session. Market basket analysis is a modeling technique based on the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. Market basket analysis can be used as a recommendation system, using company data MBA can suggest the next best product a customer is likely to purchase. This can also be used to give the user discounts on products that the user is unlikely to purchase. This problem involves predicting which previously purchased products will be in the user's next order using anonymized data from the customer's orders over time. Our model can predict whether a customer will repurchase a certain product with 87% accuracy. Market basket analysis has proven to be essential for maintaining inventory, creating promotional strategies such as cross-selling. Market basket analysis can alsobe used to make decisions about in-store and online product placement. Recommender systems have become ubiquitous in our daily lives, with uses including, but not limited to, e-commerce, entertainment, research, and academia. A recommender system is an information filtering system that predicts a user's preferences and makes suggestions based on those preferences. Popular examples include YouTube, Netflix, and Amazon, all of which offer personalized recommendations to users. These systems can collect information about a user's behavior, using that information to improve their suggestions in the future. Content-based filtering is used by recommending items based on the interests contained in a user profile and the content of the items. Alternatively, collaborativefiltering groups similar users and uses the information about the group to make recommendations to the user. This project involved the creation of a collaborative movie rating filter recommendation system to model data and predict movie ratings that have not yet been assigned by users. . At first, we extracted data from the customer database and created a dictionary for each user and the movies they watched along with their respective ratings. An artificial neural network was used, and using data to test the accuracy of the model, an RMSE value of 0.96 was found, proving that there is no overfitting in the model. Parking Solutions A parking solution system is a service in which the number of empty and occupied spaces is made available to users of a parking area in real time. In this project, we used machine learning to develop an image-based classification system to predict whether a parking space is occupied or empty, with the aim of providing this information to the end user in a web application . An Inception V3 model is trained and developed from parking lot images. Using an existing camera system, we can obtain real-time images of a parking lot, which Inception V3 model can classify the lot as occupied or vacant, and frequently update this information in the database. This information can provide extensive parking details to end users through web applications. Parking solution systems are essential for hotels, leisure and retail areas because they obtain important information on customer capacity and peak times, which can facilitate many other business decisions. By adopting this data-driven solution, a parking lot can increase its efficiency and optimize its usage. Businesses can use our service to give their customers valuable information about how busy the area is and how convenient it is for them to visit. Email Classification Spam and fraudulent emails are unsolicited messages sent to consumers, often for marketing purposes or fraudulent activities. . These messages are time-consuming, annoying and, above all, can pose a threat to the recipient. Although the "Junk" folder is used to filter these messages, more advanced spamming techniques have recently been used. As a result, our spam folders often ignore these spam messages and put recipients at risk again. Our team used supervised classification to create a spam filter, to automatically classify an email based on itscontained in “Spam” or “Ham”. Using natural language processing and Naïve Bayes classification, a fast and highly scalable method, this machine learning. Not having an accurate spam filter can put you or your business at risk of harmful viruses and malware. A good spam filter won't let important emails slip through either. It is important that every employee in a company works efficiently. By allowing spam to flow into the inbox, you will spend considerable time searching for important emails and deleting all spam. This may seem trivial in everyday life, but over months and years, hours and days can be lost. Title: Web Traffic Analysis and Forecasting Web traffic is the interaction between users and a website; how many users visit a website, which pages a user clicks on, and the time spent on a page, just to name a few. By analyzing this traffic, we can find trends, or the most and least popular pages on a website. Time series analysis and forecasting can be done on temporal web traffic data, and in this project, we have predicted the number of people who will visit. web pages in the next 61 days. SMAPE (Symmetric Mean Absolute Percent Error) was chosen as a metric to evaluate the performance of our model and was found to be 0.13. Monitoring web traffic is vital to the success of any business. This helps to understand which products and services are popular on the page. Web traffic monitoring is only effective in conjunction with analytics and forecasting, to make informed decisions to optimize a website. Understanding as well as business information helps move the panels on the web page containing the products and services to provide customers with more ease of use when they find the product/service they want. For the web page, it will increase customer usage as well as business performance. Big Data is a term used to define a very large amount of all variable information recorded by a business. Data can be considered “big” depending. Usual data processing is often inadequate and expensive for Big Data, as it requires high computing power, constant maintenance, and regular scaling. This project was designed to create a pipeline to ingest an organization's data using Big Data technologies. After transferring data from the local system to an Amazon Web Services (AWS) EC2 instance, there are two ways to go about taming big data. The first is to import the data into SQLite using Python, then use the SQLite browser, checking the schema and exporting to SQL. The SQL file can then be opened in python to run a command that inserts the data into AWS RDS. The second method follows a similar structure, but uses Hadoop HDFS and Sqoop to insert the data into AWS RDS. Using HDFS and Sqoop is the fastest method of operation, but if the data contains unusual information, Sqoop is likely to generate errors. Using SQLite and Python will minimize these errors, but with a slightly slower calculation time. The benefits of completing this project for the client include: The flexibility of being able to access the data from anywhere The system will not become slow when processing the data Since the data is not in a local system, in In the event of a failure, the data is safe. Flexible System Scaling Title: Bike Sharing Application In.

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