# Top 9 Practical Projects for Data Science Beginners

Check out the top 9 practical projects for Data Science beginners

Starting your journey in data science can be overwhelming, but realistic projects serve as the means of getting practical application of the theories learned and developing the skills that would be helpful. Below is the list of the top 9 data science projects for beginners that will help you to exercise your skills and gain a better understanding of the data science domain with contributions in analysis, visualizations, and even in machine learning.

#### 1. Exploratory Data Analysis (EDA) on a Public Dataset

Objective:

Describe the properties that can be identified in a data set or given data.

Steps:

For this task, the candidate should select a public dataset from the toolbox Kaggle or UC-MLR – University of California Irvine Machine Learning Repository.

Under this step, only clean-up data and preprocess data will have to be completed.

Tools:

Jupyter Notebook

#### 2. Sentiment analysis on social media data — Twitter

Objective:

Find out the percentage of positive, negative, and neutral tweets.

Steps:

As a continuation of the previous step, it is important to mention that the tweets should be collected with the help of the Twitter API.

It is also important to clean data by tokenization and eliminate words that do not reflect the actual sense of the context such as stop words.

Create a machine learning model for sentiment analysis using an appropriate set of algorithms.

Tools:

Twitter (Tweepy) Text (Natural Language Toolkit) Predictive Modeling (Scikit-learn)

Jupyter Notebook

#### 3. Predictive Modeling with Housing Price Data

Objective:

Propose an operation that involves developing a model that would have the capability of estimating the price of a house depending on particular attributes.

Steps:

Select a similar Dataset for the analysis as the used dataset in the following project –Boston Housing Dataset.

Feature configuration and dataset investigation.

Fine tune regression models and ascertain the degree of generalization of the models.

Tools:

Jupyter Notebook

#### 4.  Image classification using the MNIST dataset

Objective:

It is also possible to classify the images of handwritten digits with the help of such machine learning techniques as K-Nearest Neighbors, Naive Bayes, Support Vector Machines and others.

Steps:

First, load the training set from the MNIST data and perform a light preprocessing on it.

Victoria Tsang. (2017). Ideally, after fitting, we should evaluate the model accuracy and fine-tune the hyperparameters The Complete Guide to [k-nearest neighbors].

Tools:

Programming language: Python: Machine Learning frameworks: TensorFlow or Pytorch, Keras

Jupyter Notebook

#### 5. Customer Segmentation Using Clustering

Objective:

Customer relationships should be managed through segmentation that classifies customers according to their behavior in purchasing goods.

Steps:

Select one of the retail data sets.

A primary step in any data mining project is data exploration and preparation by performing exploratory data analysis and feature selection, which are as follows:

Tools:

Pandas for Data Manipulation, Scikit-learn for Machine Learning and Matplotlib for Data Visualization

Jupyter Notebook

#### 6. Time Series Forecasting with Stock Prices

Objective:

Predict future stock costs utilizing past information gathered from the market.

Steps:

Another method is to gather data concerning historical stock price quotations and other stock market figures.

Develop the discrete time models starting from ARIMA or a LSTM model.

Tools:

Jupyter Notebook

#### 7. Recommendation System for Movies

Objective:

Stake out recommendations to produce a system that will identify films that are suitable to the tastes of users.

Steps:

Specifically, using the MovieLens dataset or any other similar database, the following instructions should be followed:

Make use of collaborative filtering and content-based filtering methods.

Tools:

Jupyter Notebook

#### 8. Natural Language Processing with Text Classification

Objective:

That is, it involves categorizing text documents into a set of accumulated categories or classes.

Steps:

Select a text corpus (e.g., news with related topics in the area of your interest).

Convert text data into vector form (to use as features), data cleaning.

This is for the classification of texts and involves training and evaluating a model.

Tools:

Jupyter Notebook

#### 9. Anomaly Detection in Network Traffic

Objective:

This is distinguished by discovering patterns of behaviors or data traffic that differ significantly from normal levels.

Steps:

The net flow data set of the program can be used.

To do this, wash the data and conduct additional procedures on datasets Pre-processing and explore the data.

Tools:

Python with Additional features of Pandas, Scikit-learn, and Matplotlib

Jupyter Notebook

These practical projects make it easier for learners to gain a grasp of data science, and the titles of the topics being offered include data analysis and data visualization, machine learning, and natural language processing among others. It will also be beneficial for beginners even though the project solutions offer a greater level of difficulty, helping them strengthen the foundational knowledge of data science to be able to solve more complex problems in the future.

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