a raw dataset and create a model, summarize its results and draw conclusions.
$10-30 USD
Pagado a la entrega
1-Create/Collect supporting raw dataset as CSV file.
2-Choose a data science supervised learning approach you want to explore and implement in Python: data classification problem, regression problem or text sentiment analysis.
3-Write and turn in compiled Python script that implements the model.
4-Your script should have train/test data subsets.
5-Run your code and collect the results on model accuracy, precision and recall.
6-Write up a word document or a PDF or any like notepad document that summarizes your finding and delivers three main data insights.
7-Write and turn in the next steps you would want to do if you have more time such how to improve accuracy on the test set and tune the model for better performance. Anything that you want to add.
Nº del proyecto: #18228105
Sobre el proyecto
12 freelancers están ofertando un promedio de $64 por este trabajo
I have a good hands on working with Advanced R and Python and BI tools and technologies, AI, Big Data. I have quite a good knowledge of DL/ML Algorithm , have also developed Dashboards and Web Applications using flask/ Más
Hi, I am interested in your project. I am Python and Machine Learning specialist, certified by Freelancer. I fully understand your requirements and I am sure I can help you. Let's discuss details by chat. Thank you.
Hello, dear! I am interested in your project. I have extensive experience in data analysis such as classification and clustering, feature selection, dimensional reduction, time series analysis, data forecasting, miss Más
Hi I am a very experienced statistician, data scientist and academic writer. I have completed several PhD level thesis projects involving advanced statistical analysis of data. I have worked with data from several comp Más
"Hi, Hope you are doing well! Thanks for sharing your project requirement with us. It will be our great pleasure to work on your project. I have checked your requirement, yes we can do it, because we already work on si Más
