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Doorbell Wiring and Installation November 19, January 15, December 19, People show their appreciation as well as frustration towards a product through social media. If there is a way to understand the sentiments expressed about a product, it can be very useful. This project analyzes the tweets to understand their sentiment over time. This analysis will help Great American Insurance in their underwriting decisions.
For a Collections Team in a bank, contacting customers via calls is one of the key methods to collect a debt. The team is also required to consider various components associated with this method such as the amount of debt, the number of missed payments, available calling agents, and more. Since all customers are not equally risky in terms of loss of debt associated with them, calling all of them with equal priority is not effective, and therefore prioritizing customers is important. To utilize calling customers efficiently it is of significance to assign a priority to customers leveraging information of the customer with the bank.
In this project, I have explored account balances, information on missed payments, and probability score based on the probability of customer missing more payments in future of customer accounts to define priorities from different approaches. These priorities will be used by the bank to focus collection efforts via calls efficiently. Additionally, I have built a framework for KPI tracking which will be used to assess the working of defined priorities in the future.
As the data in every business is increasing, need to extract meaningful information from gamut of data is both a time saver and requirement for better decision making. Same is true for online entertainment platform like Netflix, Amazon Prime, Spotify and many others.
Recommender systems are in forefront to solve this problem. These systems collect information from the users to improve the future suggestions. This paper aims to describe the implementation of a movie recommender system via Content based, Collaborative filtering and Hybrid algorithms using python. The main objective of this capstone project is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. This model is part of a collaborative project, the other part being to create an object detection model.
The aim of the two parts is to design a system that can detect and recognize Vehicle Number plates. Once the license plate is detected, it would undergo processing, and the text data can easily be edited, searched, indexed and retrieved. The best model for handwritten digit classification is SVM with an accuracy score of We love Netflix for the movie recommendations it does.
Movie or content recommendation is very important for Netflix as engaging users more and more brings them more revenue. But dealing with human preferences or interests is extremely challenging. As they say, in many cases a subscriber may visit Netflix without knowing what exactly to watch. If he did not find any interesting movie in his recommendations, there is a high chance of the subscriber leaving the site.
To avoid this, recommendation is highly used to increase the customer engagement on Netflix. Each subscriber is nuanced in what brings them joy and how that varies based on the context they are set in. Moreover, tastes and preferences of customers might change over time which further complicates the recommendation process.
In this project, we focused on collaborative filtering where the behavior of a group of users is used to make recommendations to other users. Recommendation is based on the preference of other similar users. We used Surprise library, a Python scikit library for analyzing recommender systems that deal with explicit rating data.
In this digital era understanding, polarities within a text statement had emerged to be an important factor for the businesses, as they can extract the extent of customer satisfaction or even a suggestion on their product. But, humanly it is impossible to perform this.
Sentiment Analysis is the classification of human emotions like positive, negative and neutral on a text sentence. It is a text analysis method to determine the polarity within the text, a whole document, a sentence, or a paragraph. Our end goal is to build a model to predict the polarity or sentiment of a review, for that we will start with text cleaning and perform exploratory data analysis to understand the data better and then proceed to topic modeling where we try to cluster these reviews into their potential topics, once we get the topics clustered we then move onto the important segment, that is sentiment analysis, where we try to fetch polarity of each sentence review.
Our final model will be built with the help of machine learning algorithm and then we will evaluate our model with evaluation measures like accuracy, precisions, recall, etc. At the end of this project we must be in position to predict the polarity of a review for a company, as we are going build a process, the same application can be used for different datasets with some minute changes to get sentiments out of it.
A dataset of 32, songs with 12 audio features for each of the songs provided by Spotify was analyzed to determine whether these audio features could be used to classify songs into 6 different genres. Genre classification is an important task for any online music streaming service. Amongst the 3 data mining techniques i. Results indicate that genres like Rap and EDM are the easiest to classify as Rap songs are high on speechiness and EDM tracks are high on tempo and energy. People usually purchase online products after looking at how much star rating it has and after shortlisting the product, they usually read several text reviews written by other customers who have purchased this product.
E-commerce companies build recommendation engines to market specific products to a customer which have been purchased and liked by similar customers. While deciding whether a customer liked a product or not to decide whether to recommend it to similar customers , star rating given by customer can be easily utilized. But the star rating may not capture the entire sentiment of a customer about a product. In this paper, we will use text mining techniques to use the reviews written by customers to predict whether a customer will recommend a product or not.
IBM is a multinational technology company which provides products ranging from hardware and software to consulting services along with innovations through research.
Being a key player in the analytics industry, they have developed multiple game changing products to drive down costs and build up accuracy. These products have been significant for the organization as well as various clients across domains. However, for continuity in growth, its essential to retain their workforce and make the employees feel valued.
The problem is approached through performing extensive data analysis and predictive modeling to understand the key factors behind employees feeling burnout, fatigue and eventually leave. On analyzing the results, some of the changes that should be deployed by management are providing a proper career path for younger employees, monitor working hours, incentivize overtime, etc.
Global sales of pasta, pasta sauce, pancake mix, and syrup have been growing fast in recent years and are forecasted to grow even faster. The U. This thesis uses advanced statistical procedures — Time Series Analysis, K-means Clustering, and Association Rules Analysis — to detect sales trends, forecast future sales, and improve promotional strategies for these items.
Data are from the open-source Carbo-Loading database available from ARIMA models are adopted as the final models for sales forecasting.
Results suggest that zip-code clusters that complement those in Business forecasts help organizations prepare and align their objectives by providing a big picture of the future. Improving the accuracy of forecasts, hence, is one of the integral factors in the business planning of organizations.
This project explores different ways to forecast unit sales of products with the objective of zeroing in on the model with the least error. A special focus is given on ensemble models during the study.
Ensembles are recognized as one of the most successful approaches to prediction tasks. Previous theoretical studies of ensembles have shown that one of the key reasons for this performance is diversity among ensemble models. This involves recognizing the contents of an input image and a language model to turn the understanding into a meaningful sentence or words describing the image. There are various applications of Image captioning such as image indexing for Content-based Image Retrieval CBIR which in-turn has applications in e-commerce, education, ads, social media.
Deep-learning methods have demonstrated state-of-the-art results in this type of application. This model has achieved a BLEU score between 0. Natural Language processing is one of the most prominent techniques that helps us deal with unstructured data in a more effective and quick manner. The domain has various applications in tasks like machine translation, Speech to text and vice versa translation, sentiment analysis, chatbots and text classification etc.
For this project we will be exploring one its very useful application called Topic Modeling. Topic modeling is an application of NLP that helps to identify the main content of the document which could be further used to filter out the important sections quickly and effectively.
It is an unsupervised algorithm that used the document corpus matrix to identify the most relevant topic related to the document. Extracting these document topics could be very helpful in automatic labelling and clustering of the documents into major categories on which further analysis can be performed later to generate more insights about the contents of required documents. It is quite different from topic classification technique that is based on supervised learning algorithm.
Computers are good at answering questions with single, verifiable answers. But humans are often still better at answering questions about opinions, recommendations, or personal experiences. Humans are better at addressing subjective questions that require a deeper, multidimensional understanding of context - something computers are not trained to do well yet.
Questions can take many forms - some have multi-sentence elaborations while others may be simple curiosity or a fully developed problem. They can have multiple intents or seek advice and opinions. Some may be helpful and others interesting. Some are simple right or wrong.
Unfortunately, it is hard to build better subjective question-answering algorithms because of a lack of data and predictive models. This project aims to use the new dataset to build predictive algorithms for different subjective aspects of question-answering and improve automated understanding of complex question-answer content. Even a single toxic comment can derail an entire conversation and the fear of such comments often hinders people from sharing their opinions, thereby reducing the quality of an online discourse.
The Conversation AI team[1], a research group founded by Jigsaw[2] and Google builds the technology to protect such voices. The goal of this project will be to study and apply machine learning techniques to identify whether a comment is toxic or not.
By identifying the toxicity in conversations, we can deter users from posting such messages, encourage healthier conversations and have a safer, more collaborative internet across the globe.
The complexities of the health care system and the lack of comparative information about how services are accessed, provided, and paid for were the driving force behind this legislation. The goal of the APD is to serve as a key data and analytical resource for supporting policy makers and researchers. Project is planned to go in phase 2 in mid to be considered to undergo statistical analysis by using Machine Learning for predictive analysis.
The APD is creating new capability within the Department, including more advanced and comprehensive analytics to support decision making, policy development, and research, while enhancing data security by protecting patient privacy through encryption and de-identification of potentially identifying information.
With the APD, the Department will have a comprehensive picture of the health care being provided to New Yorkers by supporting consumer transparency needs on quality, safety, and costs of care. The systematic integration of data technology and weaving of the previously fragmented sources of data will create a key resource to support data analyses that address health care trends, needs, improvements, and opportunities.
Predicting attrition, whether an employee will leave the job or not, has become an important concern for the institutions in recent days, owing to several reasons. In this project, we will work on a dataset from Kaggle in R to explore the factors that are related to employee attrition through Exploratory Data Analysis and build statistical models that could be used to predict whether an employee would leave the company or not.
Finally, we will explore different sampling techniques and dimension reduction technique to find the important factors. In this paper, we look at design of financial hardship offers for various consumer loan products. Designing a financial hardship offer involves changing certain terms of an existing loan contract to make debt payments more affordable to borrowers in financial distress.
There is a delicate balance of risk and reward involved while changing the terms of a consumer loan. Hence, we use an analytical approach to balance these two quantities. We first quantify the risk using the expected loss. The reward is quantified using the net present value of the expected income cashflows. The probability of default is modelled as a logistic curve whose parameters are determined based on historical data.
The resulting objective function is a non-linear function of the decision variables. Goal and Background: The Breast Cancer Wisconsin Diagnostic data set contains the information about the features that are computed from a digitized image of a fine needle aspirate of a breast mass.
Feature variables describe characteristics of the cell nuclei present in the image. The data set contains 31 variables, one of them is Diagnosis type of the breast mass classifying them into Benign and Malignant type.
The aim of the study is to build a best model using machine learning techniques like Logistic Regression, Decision Trees and Random Forest that uses the feature variables and predicts the Diagnosis type which would help the Breast Cancer Patients to identify malignancy in the early stages of tumor.
The seed set for the random sampling of data is We will start with fitting the best logistic regression model using the Exploratory Data Analysis and variable selection techniques like Stepwise variable selection method. After fitting the logistic regression model, we will move to the tress approach for the model building.
Starting from classification tress and to more complex techniques like random forests. The model performances will be evaluated based on the Out-of-sample predictions and a final best model will be selected. Major findings: It was found that as we move from simple models like Logistic Regression and Decision Trees to the more complex models like Random Forests the prediction goes on improving reducing but we lose the interpretability.
Depending upon our goal of study i. As in our case prediction is our major concern, the random forest model was chosen as it had the best prediction for the Diagnosis of breast cancer cells.
Unsupervised learning has been the go to choice for segmenting data when labels are not readily available or are expensive to get. Unsupervised learning uses machine learning algorithms to divide the data in separate clusters. While there are a large number of methods available, not all of them are applicable to categorical data. Further, some methods require an input for the number of clusters, while other methods automatically find the optimal number of clusters.
In the field of marketing, finding the right target audience is a crucial step because of cost constraints and efficacy of marketing campaigns. If the right message is sent to the right group, it can help increase customer engagement and help generate higher profits at a lower cost. In this project, the goal is to find a segmentation of video gamers such that they have distinct qualities.
Various unsupervised clustering algorithms are applied to the categorical data. This project is aimed at applying data science and machine learning methods to study the effects of elemental composition on the performance of phase change materials PCM.
This special class of materials is actively being pursued in electronics and optoelectronics research for the realization of cutting-edge data storage and information processing technologies. We have tried to build a statistical inference method to identify highly desired and an active area of combinations of primary elements and dopants. We have extracted the independent and the dependent properties of these alloys from various published papers and have built a SQL database.
Using these parameters, data pre-processing analysis such as outlier analysis, multi-correlation analysis and EDA analysis are done to understand the data distribution better.
To conclude, we have performed a clustering analysis based on the dependent parameters to understand the influence of primary elements and dopants.
These clusters explain to us how the primary elements and dopants influence the activation energy and other dependent parameters of the crystalline, amorphous and the transition phases of these PCM alloys. Pharmaceutical industries market their products to physicians through detailing, wherein a sales representative goes to the physicians to talk about the drug and provide free samples for trial purposes.
Based on this comparison, we have provided a suggestion about whether or not to invest in ThoughtSpot for the future.
This may be because more ads were shown in Paid Social channel or physicians preferred Social channel over Search and Display. The team decided not to invest in ThoughtSpot this year because Qlik is more suited to its needs based on the existing dashboards and anticipated future needs.
Unstructured text data is everywhere on internet in the form of emails, chats, social media posts, complaint logs, and survey. Extracting texts and classifying them can generate a lot of useful insights, which can be used by businesses to enhance decision-making. Text classification is the process of categorizing text into different predefined classes. By using Natural Language Processing NLP , text classifiers can automatically analyze text and then assign a set of predefined tags or categories based on its content.
Lately, deep learning approaches are achieving better results compared to previous machine learning algorithms on tasks like image classification, natural language processing, face recognition, etc. The success of these deep learning algorithms relies on their capacity to model complex and non-linear relationships within the data.
This study would cover supervised learning models and deep learning models for multi-class text classification and would investigate which methods are best suited to solve it. The classifier assumes that each new complaint is assigned to one and only one category. Forecasting stock return is an important topic in the finance industry. However, the stock market has high volatility which makes the price movements hard to be predicted.
The traditional Fama-French three-factor model applied the conventional multiple linear regression model, which is still powerful in evaluating stocks and comparing investment results when stocks are held for different periods.
However, in recent years, machine learning methods are taking advantage of calculating speed and forecast accuracy. Therefore, in this project, we will evaluate the model performance for both traditional linear models and machine learning models. In this project, we applied multiple linear regression, univariate linear regression, random forest, XGBoost, and Artificial Neural Network, models.
All models selected Market Excess Return Mkt. However, machine learning methods are not able to outperform linear models in terms of output accuracy. We will in the end, briefly discuss the possible reasons and project limitations. The coronavirus COVID pandemic situation is unprecedented and catastrophic and since its inception in China last year, the anticipation regarding its impact and cure is ineffective.
This virus has put a lot of things on hold, leading to lockdown and complete shutdowns in most of the countries for at least a month. Time-series forecasting is one of the most encountered applications in the Data world. Modeling a time series and predicting future values is an important skill.
One simple but powerful method for analyzing and predicting a time series is the additive model. For our study, we have considered Airline industry performance amidst the pandemic and have built different additive models for the time-series data using the Prophet package developed by Facebook for time series forecasting. As our study captures the impact of Covid on the airline industry, so we have selected the model containing data points from February to May , without splitting further in training and test records.
The OEOA is responsible for monitoring and auditing the workforce activities taking place across the university to ensure that they are compliant with the Affirmative Action Plan. This project aims to analyze 5 years of Applicant Flow Logs describing the candidates for every position that the University hired in this time.
This data includes information about the candidates regarding their status of 2 protected classes :. Currently, the proportion of employees belonging to these protected classes in the University of Cincinnati are significantly lower across most job groups and business units, compared to the proportions in relevant pools provided by the U.
Department of Labor. By analyzing the given data, this project aims to develop a strategic action plan for improving the representation of these protected groups by understanding where in the hiring process recruitment, interviews, selection are barriers present for such groups. Whether you shop from meticulously planned grocery lists or let whimsy guide your grazing, our unique food rituals define who we are. Instacart, a grocery ordering and delivery app, aims to make it easy to fill your refrigerator and pantry with your personal favourites and staples when you need them.
After selecting products through the Instacart app, personal shoppers review your order and do the in-store shopping and delivery for you.
Currently they use transactional data to develop models that predict which products a user will buy again, try for the first time, or add to their cart next during a session. I shall be using association rule mining techniques to figure out and obtain the following results for the business:.
A major responsibility of IT Teams is to not only provide users with faster service but also provide a positive experience during the whole engagement. The objective of this project is to improve user experience while availing IT services for the employees across all locations of the company. We aim to reduce the time to resolution of tickets and increase customer satisfaction. The first part of the project focuses on predicting ticket types for users that engage IT support using emails.
The email text data is analyzed using natural language processing techniques and then using machine learning algorithms, segregated into the correct ticket type. The second part is targeted at creating a personalized experience by using personas for all the IT service users to better understand their behaviors and addressing their needs based on the insights discovered.
The users are clustered together based on dimensions such as channel of approach, ticket categories and the ticket impact using K-Prototype algorithm. This segregation will provide insights into what to target first and what kind of recommendations can be given to help improve the experience for certain users. The biggest problem a company faces is of Churned Customers. Churning is a term used in this industry to describe whether the consumer or the user is going to continue the services with the company any further or not.
By being aware of and monitoring churn rate, companies are equipped to determine their customer retention success rates and identify strategies for improvement. In this project, we will work on customer churn dataset of a telecom industry and will use different machine learning models to understand the precise customer behaviors and attributes which signal the risk and timing of customer churn.
After collecting data points related to telecom industry, a rule-based quality-control method is designed to decrease human error in predicting customer churn. After examining the results from different machine learning models, we conclude that the results using XGBoost model are promising: we achieve an accuracy rate of There is a growing economic inequality among individuals across the world which has been a concern for various governments.
Many people consider their income information private and would be hesitant to share the information. Download link is provided for Students. Wilson The text is designed for undergraduate Mechanical Engineering courses in Kinematics and Dynamics of Machinery.
It is a tool for professors who wish to 64bdbb59a4 47 jayakumar pdf is free to play, and the Settings menu offers Kinematics is the study of motion, without considering the forces which produce that motion. Kinematics of machines deals with the study of the relative motion of machine parts.
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