China Home Kitchen Electrical Appliance: YoY: Number of Loss Making Enterprise data was reported at 10.169 % in Oct 2015. This records an increase from the previous number of 9.600 % for Sep 2015. China Home Kitchen Electrical Appliance: YoY: Number of Loss Making Enterprise data is updated monthly, averaging 10.638 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 46.154 % in Dec 2011 and a record low of -27.400 % in May 2010. China Home Kitchen Electrical Appliance: YoY: Number of Loss Making Enterprise data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIA: Home Electrical Apparatus: Home Kitchen Electrical Appliance.

China Home Kitchen Electrical Appliance: Number of Loss Making Enterprise data was reported at 130.000 Unit in Oct 2015. This records a decrease from the previous number of 137.000 Unit for Sep 2015. China Home Kitchen Electrical Appliance: Number of Loss Making Enterprise data is updated monthly, averaging 120.000 Unit from Dec 2003 (Median) to Oct 2015, with 97 observations. The data reached an all-time high of 217.000 Unit in May 2009 and a record low of 46.000 Unit in Dec 2003. China Home Kitchen Electrical Appliance: Number of Loss Making Enterprise data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIA: Home Electrical Apparatus: Home Kitchen Electrical Appliance.

Here are a few use cases for this project:

1. Grocery Inventory Management: The Onion Detector can be used in supermarkets and grocery stores to automatically monitor and manage the inventory and stock of onions by accurately identifying and counting the onions in the storage area or on display shelves.

2. Onion Harvesting Automation: Developing harvest automation equipment using the Onion Detector model can help farmers and agricultural companies to detect and separate onions from weeding plants or soil, significantly improving the speed and efficiency of onion harvesting processes.

3. Quality Control in Food Industry: The Onion Detector can be integrated into the production line of food processing plants, enabling the system to automatically detect onions in various stages of processing—such as sorting, cleaning, and grading—to ensure a consistent quality of the final product.

4. Onion Waste Reduction: The model can be used in a retail, restaurant, or home setting to identify onions that may be starting to spoil, enabling consumers or foodservice operators to prioritize using these onions before they need to be discarded, ultimately limiting food waste.

5. Smart Kitchen Assistance: By integrating the Onion Detector into smart kitchen appliances, users could receive automatic recipe suggestions based on the available ingredients, including onions, making it easier to determine meal options without manually searching recipe databases.

Here are a few use cases for this project:

1. Agricultural Automation: This model can be utilized in smart agriculture projects for automatic crop harvesting. When installed in automated harvesting machines, the model can identify the maturity level of tomatoes, ensuring only ripe ones are harvested and reducing unnecessary waste.

2. Grocery Quality Control: The model can streamline the sorting process in grocery stores. By scanning tomatoes, it can sort them based on ripeness level, making it easier for customers to select the appropriate ones and for store workers to remove overripe or rotten ones.

3. Food Processing Industries: In industries such as ketchup, sauce, or juice making, this model could ensure only ripe tomatoes make it to the production line, enhancing product quality and reducing waste.

4. Smart Refrigerators: Home appliance makers can incorporate this model into smart refrigerators, helping users to effectively manage their food. They’d know when their tomatoes are ripe and ready to eat, when they are overripe and should be used quickly, or when they are rotten and need to be thrown away.

5. Cooking Aid Application: This model can be integrated into a kitchen or cooking app to advise users on the optimal use of their tomatoes based on their ripeness, helping them to produce better tasting food and reduce waste.

About Flipkart:

Flipkart is an Indian e-commerce company, headquartered in Bangalore, Karnataka, India. It is the largest e-commerce company in India and was founded by Sachin and Binny Bansal. The company has wide variety of products electronics like laptops, tablets, smartphones, and mobile accessories to in-vogue fashion staples like shoes, clothing and lifestyle accessories; from modern furniture like sofa sets, dining tables, and wardrobes to appliances that make your life easy like washing machines, TVs, ACs, mixer grinder juicers and other time-saving kitchen and small appliances; from home furnishings like cushion covers, mattresses and bedsheets to toys and musical instruments.

Mobile Phones

Mobile phones are one of the most rapidly rising industries, as well as one of the most prominent industries in the technology sector. The rate of increase has been exponential, with the number of mobile phone customers increasing fivefold in the last decade. Globally, the number of smartphones sold to end users climbed from 300 million in 2010 to 1.5 billion by 2020.

Flipkart and Mobile Phones

As previously stated, mobile phones are in high demand and are one of the ideal products for a novice to sell. Flipkart will be the ideal spot for a vendor to market their stuff because its reach.

Content

The dataset contains description of top 5 most popular mobile brand in India. Columns : There are 16 columns each having a title which is self explanatory. Rows : There are 430 rows each having a mobile with at least a distinct feature.

Acknowledgements

The data was retrieved directly from Flipkart website using some web crawling techniques

Assumption

We don’t have direct sales report of how many units of a mobile model was sold. In general, number of people rating a product is directly proportional to number of units sold. So, for the purpose of the solution, we are using number of people rating the product as the equivalent units sold.

Inspiration

The objective is to address a hypothetical business problem for a Flipkart Authorized Seller. According to the hypothesis the individual is looking to sell mobile phones on Flipkart. For this, the individual is looking for the best product, brand, specification and deals that can generate the most revenue with the least amount of investment and budget constraints.

Questions to be answered: 1. Whether he should sell product for a particular brand only or try to focus on model from different brands? 2. Using EDA and Data Visualization find out insights and relation between different features 3. Perform detailed analysis of each brand. 4. Assuming a budget for the problem come to a solution with maximum return.