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$","1.$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 techniques to handle missing values in the dataset. $4.$Apply a moving average technique with appropriate window size to smooth out the data and observe any patterns or trends. Present the plot of each feature with a moving average line to show the technique's 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.