$1.$ 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.

$2.$ Identify and describe missing values.

$3.$ Apply appropriate technique to handle missing values in the dataset.

$4.$ 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.

$5.$ Identify and describe outliers using histograms.

$6.$ Perform correlation analysis on the cleaned dataset. Identify relevant variables and calculate their correlation coefficients. Interpret the correlation coefficients to understand the relationships between variables.

$7.$ Discuss the potential impact of the data quality issues on subsequent analysis or decision$-$making if left unaddressed. Explain how the data cleaning process contributes to ensuring reliable and accurate insights from the dataset.