Closed abhisheks008 closed 3 weeks ago
Full name : Ankana Pari GitHub Profile Link : https://github.com/ankana2113 Participant ID (If not, then put NA) : couldn't find Approach for this Project : Would plot some graphs to determine the trend of the data and then try to approach which ml model fits best for e.g, regression or random forest etc. What is your participant role?KWoC
Issue assigned to you. Go ahead! @ankana2113
I am facing a problem on how to convert datetime obj into a string because standardizing using StandardScaler requires a string. Can you please help me out? Ankana Issue #440
On Fri, Dec 8, 2023 at 7:57β―PM Abhishek Sharma @.***> wrote:
Assigned #440 https://github.com/abhisheks008/ML-Crate/issues/440 to @ankana2113 https://github.com/ankana2113.
β Reply to this email directly, view it on GitHub https://github.com/abhisheks008/ML-Crate/issues/440#event-11194138289, or unsubscribe https://github.com/notifications/unsubscribe-auth/BCJWMCYOGMAX5E7XHZFLMTDYIMPTNAVCNFSM6AAAAAA76ZZH2WVHI2DSMVQWIX3LMV45UABCJFZXG5LFIV3GK3TUJZXXI2LGNFRWC5DJN5XDWMJRGE4TIMJTHAZDQOI . You are receiving this because you were assigned.Message ID: @.***>
Can you share the problem in detail. You can connect with me in Discord.
Full name : Mohd Mudassir Ansari GitHub Profile Link : Mudassir-A Participant ID (If not, then put NA) : NA Approach for this Project : Gain insigths on Sales, Trends, Product, Revenue, Concerns and Customer Retention by visualising and analysing the given Data. What is your participant role? : JWOC
Issue assigned to you @Mudassir-A
Aditi Kala Github:- https://github.com/why-aditi Participation ID:- NA Approach: Clean and preprocess the data, handling missing values and ensuring all variables are in suitable formats. Conduct exploratory data analysis to understand relationships and patterns in the data. Select relevant features that influence sales, possibly using feature engineering techniques. Choose appropriate regression algorithms like Random Forest or Gradient Boosting, train the model on a portion of the data, and evaluate its performance using metrics such as MSE. Interpret the model's results to extract insights into key drivers of sales, such as the impact of car features or economic conditions. Visualize actual versus predicted sales values to assess model accuracy. Participation Role:- SSOC Season 3
Aditi Kala Github:- https://github.com/why-aditi Participation ID:- NA Approach: Clean and preprocess the data, handling missing values and ensuring all variables are in suitable formats. Conduct exploratory data analysis to understand relationships and patterns in the data. Select relevant features that influence sales, possibly using feature engineering techniques. Choose appropriate regression algorithms like Random Forest or Gradient Boosting, train the model on a portion of the data, and evaluate its performance using metrics such as MSE. Interpret the model's results to extract insights into key drivers of sales, such as the impact of car features or economic conditions. Visualize actual versus predicted sales values to assess model accuracy. Participation Role:- SSOC Season 3
Implement 5-6 models for this project.
Assigned @why-aditi
Hello @why-aditi! Your issue #440 has been closed. Thank you for your contribution!
ML-Crate Repository (Proposing new issue)
:red_circle: Project Title : Automobile Sales Data Analysis and Prediction :red_circle: Aim : The aim of this project is to create a machine learning model to predict the sales of the automobiles and prepare a data analysis of the same. :red_circle: Dataset : https://www.kaggle.com/datasets/ddosad/auto-sales-data :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
π Follow the Guidelines to Contribute in the Project :
requirements.txt
- This file will contain the required packages/libraries to run the project in other machines.Model
folder, theREADME.md
file must be filled up properly, with proper visualizations and conclusions.:red_circle::yellow_circle: Points to Note :
:white_check_mark: To be Mentioned while taking the issue :
Happy Contributing π
All the best. Enjoy your open source journey ahead. π