You will get a fantastic deliverable that involves Tableau, Pandas and Python.
You will get a fantastic deliverable that involves Tableau, Pandas and Python.
Project details
We are committed to providing you with a comprehensive data analysis solution for your dataset. Our approach involves a step-by-step analysis addressing each case outlined in your problem statement. We will leverage Tableau to create visualized reports and a dashboard that succinctly presents key insights. Below is an outline of the process
Data Tool
pandasWhat's included
| Service Tiers |
Starter
$200
|
Standard
$300
|
Advanced
$400
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 28 days |
Number of Revisions | Unlimited | Unlimited | Unlimited |
About Aditya
Data Analyst/Scientist with Software developer experience
Lucknow, India - 2:24 am local time
I am skilled at
1) Data Visualization and Data Cleaning
2) Creating machine learning pipelines
3) Data preprocessing
4) Feature engineering
5) Feature selection
6) Data scraping
7) Data analysis
8) Flask API
9) Tableau
10) SQL
11) Deep Learning models and hyperparameter tuning
12) React js
13) Node js
Tableau, Python, BeautifulSoup, Selenium, Scrapy, Requests, Pandas, Matplotlib,Seaborn, Numpy, Sci-kit Learn, Hugging Face ,Machine Learning, Flask, Forecasting, MS-Excel, MYSQL Workbench, SQL Server, NLP, YOLO, Artificial Neural networks, Convolutional neural networks
What do I offer:
1- 100% satisfaction
2- Unlimited Revisions
3- 24/7 support
4- 5 Days of free work After delivery
So it's time to connect and do some DATA WORK.
Steps for completing your project
After purchasing the project, send requirements so Aditya can start the project.
Delivery time starts when Aditya receives requirements from you.
Aditya works on your project following the steps below.
Revisions may occur after the delivery date.
Defining the problem
Clearly articulate the problem we are trying to solve or the question we are trying to answer through data analysis.
Data Collection, Data Cleaning and Preprocessing
Gather relevant data from various sources. Clean the data to handle missing values, outliers, and inconsistencies. Transform and preprocess the data as needed, including data normalization, scaling, and encoding.