Statistics Projects
Statistics forms the cornerstone of advanced machine learning algorithms, enabling the capture and interpretation of data patterns into actionable insights. This discipline involves the collection, examination, interpretation, and conclusion-drawing from data. Techniques like Hypothesis Testing and A/B Testing are frequently applied in making business decisions and evaluating options. Through my projects, I bring the practical applications of statistics to the forefront of data analysis.
-
Thoroughly evaluated the parameters that would be utilized to develop a machine learning model for forecasting real estate prices in Barcelona.
-
Organized the data into training and testing subsets, and transformed categorical variables into numerical representations (dummy variables).
-
Conducted rigorous statistical analysis, including checks for normality, constant variance, linear fit, and P-value, to ensure the validity and reliability of the data.
-
Selected the most statistically significant variables for use in predicting real estate prices in Barcelona.
-
The advantage of hiring experts to make financial predictions was evaluated based on the cost and potential benefits. It was concluded that the financial resources saved could be better utilized in managing other risks.
-
Regression was used to evaluate each forecaster's prediction accuracy.
-
Multiple regression was applied to compare the accuracy of different pairs of forecasters, and the one with the lowest accuracy was identified for potential removal.
-
Although accuracy is important, it was noted that having a diverse range of opinions and perspectives is also valuable. Different views can help mitigate risk by considering various factors and can be more valuable than just having accurate predictions.
