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Exploratory Data Analysis with Python

In this assignment, you have been provided with a dataset that contains quantity, sales, and profit as a synthetic dataset designed to simulate real$-$world scenarios. However, the dataset is not perfect and presents some challenges that you must overcome. It contains missing values and outliers, which cah significantly impact your analysis and predictions. Your task is to navigate through these obstacles and extract meaningful information from the data.

Open a new Google Colab notebook and import the necessary libraries.

Upload the dataset into the notebook.

Open the provided dataset on Colab and perform an exploratory data analysis $($EDA$)$ on the dataset in the following steps:

Analyze the dataset. Describe the overall trend, identify any upward, downward, cyclic or seasonal patterns, and explain your insights on the data's implications and influencing factors.

Identify and describe missing values.

Apply appropriate technique to handle missing values in the dataset.

Apply a moving average technique with appropriate windows size to smooth out the data and observe any patterns or trends. Present the plot of the each feature with moving average line to show the technique effectiveness. Describe the overall trend.

Identify and describe outliers using histograms.

$309$ words

English $($Canada$)$

Accessibility: Investigate

Focus

class activity$-5.$pdf $(153\mathrm{.}2KB)$