Beginner projects made with help of YouTube tutorials and Kaggle.

This project was done to analyze sales data: to identify trends, top-selling products, and revenue metrics for business decision-making. I did this project offered by MeriSKILL, to learn more and be exposed to real-world projects and challenges that will provide me with valuable industry experience and help me develop my data analytical skills.https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20837845%2Fe3561db319392bf9cc8b7d3fcc7ed94d%2F2019%20Sales%20dashboard.png?generation=1717273572595587&alt=media" alt=""> More on this project is on Medium

Hi, i just finished my second project to improve my coding skills which is a simple to-do-list program. It runs perfectly on my visual studios, although it's quite problematic to run the output in Kaggle. As a beginner, i used the file input output, looping, string-formatting, conditions and much more information learned and implemented in it. I hope i can receive ideas or opinions on how to improve it hehe. Please take your time to use it at your leisure.

Dataset

This dataset was created by Deeksha3@

Contents

The objective of this report is to analyze the role of small businesses in the Michigan job market using the provided dataset. We aim to understand the impact of small businesses on employment, sales, and other economic factors. This analysis will help in identifying trends and patterns that can inform policy decisions and support for small businesses.

Dataset

This dataset was created by Priya_ds

Released under Other (specified in description)

Contents

a

Vrinda Store: Interactive Ms Excel dashboardVrinda Store: Interactive Ms Excel dashboard Feb 2024 - Mar 2024Feb 2024 - Mar 2024 The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022?

And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022? And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel Skills: Data Analysis · Data Analytics · ms excel · Pivot Tables

Dataset

This dataset was created by Naimul Hasan Shadesh

Released under Apache 2.0

Contents

**Summary ** This dataset contains over 2,600 circuit projects scraped from Instructables, focusing on the "Circuits" category. It includes project titles, authors, engagement metrics (views, likes), and the primary component used (Instruments).

** How This Data Was Collected**

I built a web scraper using Python and Selenium to gather all project links (over 2,600 of them) by handling the "Load All" button. The full page source was saved, and I then used BeautifulSoup to parse the HTML and extract the raw data for each project.

Data Cleaning (The Important Part!)

The raw data was very messy. I performed a full data cleaning pipeline in a Colab notebook using Pandas.

• Converted Text to Numbers: Views and Likes were text fields (object).
• Handled "K" Values: Found and converted "K" values (e.g., "2.2K") into proper numbers (2200).
• Handled Missing Data: Replaced all "N/A" strings with null values.
• Mean Imputation: To keep the dataset complete, I filled all missing Likes and Views with the mean (average) of the respective column.

Key Insights & Analysis

1. "Viral" Effect (High Skew): The Views and Likes data is highly right-skewed (skewness of ~9.5). This shows a "viral" effect where a tiny number of superstar projects get the vast majority of all views and likes.

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1. Log-Transformation: Because of the skew, I created log_Views and log_Likes columns. A 2D density plot of these log-transformed columns shows a strong positive correlation (as likes increase, views increase) and that the most "typical" project gets around 30-40 likes and 4,000-5,000 views. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F29431778%2Fd90e2039f1be11b53308ab7191b10954%2Fdownload%20(1).png?generation=1763013545903998&alt=media" alt="">

2. Top Instruments: I've also analyzed the most popular instruments to see which ones get the most engagement. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F29431778%2F19fca1ce142ddddc1e16a5319a1f4fc5%2Fdownload%20(2).png?generation=1763013562400830&alt=media" alt="">

Column Descriptions

• Title: The name of the project.
• Project_Admin: The author/creator of the project.
• Image_URL: The URL for the project's cover image.
• Views: The total number of views (cleaned and imputed).
• Likes: The total number of likes/favorites (cleaned and imputed).
• Instruments: The main component or category tag (e.g., "Arduino", "Raspberry Pi").

Dataset

This dataset was created by Ibritics

Contents

Real Estate Database

This is a mock-up of a real estate company, this is based on an actual company that had a number of challenges - collection and revenue is the biggest issue. A deep dive into the available data will provide the possible reasons and is the purpose of the data analytics project.

Here's the fictional business scenario:

Ms. Aurora Sanchez, the Chief Operations Officer (COO) of Prime Estate talked to the operations data analyst team to discuss a couple of her requirements. Ms. Sanchez is responsible for sales, property and project management, customer service, collections, and several other operations departments under her umbrella. When she joined the organization in late 2018, she quickly got several escalations from buyers who were complaining about units, properties that were not turned over on time, and delays in the projects. Ms. Sanchez also noted problems with collections not meeting the targets, and inconsistent sales performance.

As the COO, Ms. Sanchez wants to identify and validate the history of these problems as well as see if there have been improvements in these pain points ever since she joined Prime Estate. Her focus points are Collections, Project Management, Customer Service, Collections, and Sales.

As the Business/Data Analyst Lead, your responsibility is to gather the performance data related to this part of operations, find trends, present findings, and provide recommendations that will help the organization improve the pain points of operations. You must work with the manager of customer service and collections, and the project and property management managers for this undertaking.

The data that is available is an inventory database that includes a listing of all projects, properties, their cost, package price, current status, and sales date. Another database provided is the project management database that tracks the construction initiation, time lapsed till the project is at 90% completion, and another date that tags it at 100% completed. Lastly, the collections database includes a listing of all units that are tagged as sold and tracks the turnover date (the date that the unit was turned over to the owner), collection date (the date that the full amount was based on the package price and all other charges) was collected from the buyer through multiple channels.

Tool: Microsoft Excel

Dataset: Coffee Sales

Process: 1. Data Cleaning: • Remove duplicates and blanks. • Standardize date and currency formats.

1. Data Manipulation: • Sorting and filtering function to work
   with interest subsets of data. • Use XLOOKUP, INDEX-MATCH and IF
   formula for efficient data manipulation, such as retrieving, matching and organising information in spreadsheets

2. Data Analysis: • Create Pivot Tables and Pivot Charts with the formatting to visualize trends.

3. Dashboard Development: • Insert Slicers with the formatting for easy filtering and dynamic updates.

Highlights: This project aims to understand coffee sales trends by country, roast type, and year, which could help identify marketing opportunities and customer segments.

Context

This dataset is used to practice Pandas for beginners

Content

This dataset is presented with some errors which is needed to be fixed. You can use this dataset to practice: Cleaning NaN values with basic Pandas techniques.

Acknowledgements

I have this dataset from w3school

heyy this is beginner project of python .This dataset help you to practise your projects and you can learn all . Actually i am also practise this So,you can also take the help from Youtube to learn more. Thankyou

Home Value Insights: A Beginner's Regression Dataset

This dataset is designed for beginners to practice regression problems, particularly in the context of predicting house prices. It contains 1000 rows, with each row representing a house and various attributes that influence its price. The dataset is well-suited for learning basic to intermediate-level regression modeling techniques.

Features:

1. Square_Footage: The size of the house in square feet. Larger homes typically have higher prices.
2. Num_Bedrooms: The number of bedrooms in the house. More bedrooms generally increase the value of a home.
3. Num_Bathrooms: The number of bathrooms in the house. Houses with more bathrooms are typically priced higher.
4. Year_Built: The year the house was built. Older houses may be priced lower due to wear and tear.
5. Lot_Size: The size of the lot the house is built on, measured in acres. Larger lots tend to add value to a property.
6. Garage_Size: The number of cars that can fit in the garage. Houses with larger garages are usually more expensive.
7. Neighborhood_Quality: A rating of the neighborhood’s quality on a scale of 1-10, where 10 indicates a high-quality neighborhood. Better neighborhoods usually command higher prices.
8. House_Price (Target Variable): The price of the house, which is the dependent variable you aim to predict.

Potential Uses:

1. Beginner Regression Projects: This dataset can be used to practice building regression models such as Linear Regression, Decision Trees, or Random Forests. The target variable (house price) is continuous, making this an ideal problem for supervised learning techniques.

2. Feature Engineering Practice: Learners can create new features by combining existing ones, such as the price per square foot or age of the house, providing an opportunity to experiment with feature transformations.

3. Exploratory Data Analysis (EDA): You can explore how different features (e.g., square footage, number of bedrooms) correlate with the target variable, making it a great dataset for learning about data visualization and summary statistics.

4. Model Evaluation: The dataset allows for various model evaluation techniques such as cross-validation, R-squared, and Mean Absolute Error (MAE). These metrics can be used to compare the effectiveness of different models.

Versatility:

• The dataset is highly versatile for a range of machine learning tasks. You can apply simple linear models to predict house prices based on one or two features, or use more complex models like Random Forest or Gradient Boosting Machines to understand interactions between variables.

• It can also be used for dimensionality reduction techniques like PCA or to practice handling categorical variables (e.g., neighborhood quality) through encoding techniques like one-hot encoding.

• This dataset is ideal for anyone wanting to gain practical experience in building regression models while working with real-world features.

Hello! Welcome to the Capstone project I have completed to earn my Data Analytics certificate through Google. I chose to complete this case study through RStudio desktop. The reason I did this is that R is the primary new concept I learned throughout this course. I wanted to embrace my curiosity and learn more about R through this project. In the beginning of this report I will provide the scenario of the case study I was given. After this I will walk you through my Data Analysis process based on the steps I learned in this course:

1. Ask
2. Prepare
3. Process
4. Analyze
5. Share
6. Act

The data I used for this analysis comes from this FitBit data set: https://www.kaggle.com/datasets/arashnic/fitbit

" This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. "

Dataset

This dataset was created by acmgyt

Released under MIT

Contents

from https://nlp.stanford.edu/projects/glove/

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Tabular dataset for data analysis and machine learning practice. The dataset is about the market and is usable for Power BI practice and data science.

Dataset

This dataset was created by Vicky Nayak

Contents