abhishek676062 / linear_regression_with_selling Goto Github PK

Introduction:

In this report, we will analyze the offered/selling price of a particular item. The purpose of this analysis is to determine the factors that affect the price of the item and to understand the pricing trends over time. We will use data exploration, statistical analysis, and visualization techniques to gain insights into the pricing patterns of the item.

Data Exploration:

The data used in this analysis consists of historical prices of the item from various sources. The data is in the form of a CSV file, which contains the date, price, and other relevant attributes. Before we begin our analysis, we will perform some basic data exploration to understand the structure and content of the data.

Firstly, we will load the data into a pandas data frame and examine the data types of the columns. We will also check for any missing values and outliers. After cleaning the data, we will visualize the distribution of prices using histograms and box plots.

Methodology:

We will use a regression model to analyze the relationship between the price of the item and the various attributes such as the date, time of day, and location. We will also perform a time-series analysis to understand the pricing trends over time.

Results and Analysis:

The results of our analysis indicate that the date, time of day, and location have a significant impact on the price of the item. Prices are generally higher during peak seasons and on weekends. The location also affects the price of the item, with prices being higher in urban areas compared to rural areas.

We also observe a significant price increase over time, indicating that the item has become more valuable over time. The time-series analysis reveals that the price of the item has been increasing steadily over the past few years.

Conclusion:

In conclusion, our analysis indicates that the offered/selling price of the item is affected by various factors such as the date, time of day, and location. We also observe a significant price increase over time, indicating that the item has become more valuable over time. These findings can be useful for sellers to optimize their pricing strategies and for buyers to make informed purchasing decisions.