Contact for Admission

Arya College Cousellor Arun Arya

Prof. (Dr.) Arun Arya

Contact for Admission

Arun College Cousellor Arya

Prof. (Dr.) Arun Arya

Top data science projects for a beginner in 2020

Data Science Projects for Beginners

Gradually, the countries are opening in baby steps. Even after this, it is important to learn new skills, read more books, and improve yourself. Students of top engineering colleges have a high interest in data analytics, data science, and all the related data. Here undermentioned the list of top data science projects to do during your spare time.

Credit Card Fraud Detection

The number of credit card owners expected to 1.2 billion by 2022. In order to ensure the security of credit card transactions, it is essential to monitor fraudulent activities by the professionals of Top Engineering Colleges Rajasthan. Credit card companies shall be able to recognize fraudulent credit card transactions. So that customers are not charged for items that they did not purchase.

A credit card dataset contains a mix of fraud and non-fraudulent transactions. The target predicts whether a given test transaction is fraudulent or not. Students of Best BTech College can use certain algorithms include Logistic Regression, Decision trees, Neural networks, or so on.

Customer Segmentation

Customer Segmentation is the process of splitting a customer base into multiple groups of individuals that share a similarity in ways a product is. Also, they can be marketed to them like gender, age, interests, demographics, economic status, geography, behavioral patterns, spending habits, and much more.

Customer Segmentation is one of the most significant applications of unsupervised learning. So, companies can identify the several segments of customers that allow them to target the potential user base by using clustering techniques. Companies use the clustering process to foresee or map customer segments with similar behavior to identify and target a potential user base. Students of top engineering colleges can use certain algorithms under this that include the Partitioning method, Fuzzy clustering, Density-based clustering, and Model-based clustering.

Furthermore, once the data is collected, companies or professionals of best engineering colleges can gain a deeper understanding of customer preferences and requirements for discovering valuable segments that would reap the maximum profit. In this way, they can strategize their marketing techniques more efficiently and minimize the possibility of risk to their investment.

Sentiment Analysis

The sentiment is defined as a view or an attitude toward a situation or event. It is a vital topic in the field of Data Science. It has utmost relevance in today’s age of social media and it can solve the number of business problems.

With the help of sentiment analysis, students of Engineering Colleges in India can find out the nature of opinion reflected in documents, websites, social media timelines, etc. Humans are ought to have a range of sentiments from happy, sad, angry, positive or negative, depressed, hatred, love, and more.

In today’s time, any data-driven organization would have to absorb outcomes from the sentiment analysis model. It helps them to determine the attitude of their consumers and target customers towards the products or services. Twitter sentiment analysis is a model that HAS to be run all time. Some of the intelligence agencies perform sentiment analysis in the form of certain algorithms including Naive Bayes, Decision trees, Package Tidytext.

Speech Emotion Recognition

A human can do certain activities. A lot of them can be governed by speech and the emotions attached to a scene, a product, or experience. Speech Emotion Recognition can be a compelling Data Science projects to do for the students of the Best BTech Colleges. It attempts to perceive human emotions from the speech. In addition, different sound files used as the dataset for sighting human emotion.

SER essentially focuses on feature extraction to extract emotion from audio recordings. While working on the Python, students of Engineering Colleges in Rajasthan can use certain algorithms like Convolution Neural Network, Recurrent neural networks, Neural Network, Gaussian mixture model, Support Vector Machine.

Predictive Analytics

The purpose of predictive analytics is to make predictions about unknown events of the future. Also, it encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining; analyze current and historical facts to identify risks and opportunities. For instance, Customer Relationship Management, Clinical decision support systems, Customer and Employee Retention: churn rates, Project Risk Management.

Time-series Analysis and Modeling

Time series, a series of data points indexed, listed, or graphed in time order. One of the most commonly used techniques in data science with a wide range of applications. Also, it ranges from weather forecasting, predicting sales, analyzing year trends, predicting tractions, website traffic, competition position, etc.

Students of Top BTech Colleges can analyze the numbers of the future through Business houses; time and again work on time series data. Therefore, from time series analysis, they can look into ads watched per hour, in-game currency spends per day, change in product trends, etc.

Regression Analysis

The purpose of regression analysis is to predict an outcome based on historical data. Regression analysis is a robust statistical test that enables examination of the relationship between two or more variables of interest. While there are many types of regression analysis, all examine the influence of one or more independent variables on a target variable.

Exploratory Data Analysis

Exploratory Data Analysis is actually the first step in a data analysis process. So, students of top engineering colleges must figure out the questions they want to ask, how to frame them, best manipulate it to get the answers needed.

EDA exposes a broad look of patterns, trends, outliers, unexpected results and more, especially in an existing data using visual and quantitative methods.

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