percentile ( prices, 25 ) third_quartile = np. std ( prices ) # Alternative using pandas # std_price = prices.std(ddof=0) # There are other statistics you can calculate too like quartiles first_quartile = np. median ( prices ) # Alternative using pandas # median_price = dian() # TODO: Standard deviation of prices of the data std_price = np. mean ( prices ) # Alternative using pandas # mean_price = an() # TODO: Median price of the data median_price = np. max ( prices ) # Alternative using pandas # maximum_price = prices.max() # TODO: Mean price of the data mean_price = np. min ( prices ) # Alternative using pandas # minimum_price = prices.min() # TODO: Maximum price of the data maximum_price = np. # TODO: Minimum price of the data minimum_price = np. You will know the dataset loaded successfully if the size of the dataset is reported.
Run the code cell below to load the Boston housing dataset, along with a few of the necessary Python libraries required for this project.
The feature 'MEDV' has been multiplicatively scaled to account for 35 years of market inflation. The remaining non-relevant features have been excluded. The features 'RM', 'LSTAT', 'PTRATIO', and 'MEDV' are essential. This data point can be considered an outlier and has been removed. 1 data point has an 'RM' value of 8.78. These data points likely contain missing or censored values and have been removed. 16 data points have an 'MEDV' value of 50.0. For the purposes of this project, the following preprocessing steps have been made to the dataset: The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts. The dataset for this project originates from the UCI Machine Learning Repository. This model would prove to be invaluable for someone like a real estate agent who could make use of such information on a daily basis. A model trained on this data that is seen as a good fit could then be used to make certain predictions about a home - in particular, its monetary value. In this project, you will evaluate the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts. Training a Smart Cab (Reinforcement Learning). Identifying Customer Segments (Unsupervised Learning). Building a Student Intervention System (Supervised Learning). Boston House Prices Prediction and Evaluation (Model Evaluation and Prediction). Efficiently Searching Optimal Tuning Parameters. Cross-Validation for Parameter Tuning, Model Selection, and Feature Selection. Dimensionality Reduction and Feature Transformation. K-nearest Neighbors (KNN) Classification Model. 
Vectorization, Multinomial Naive Bayes Classifier and Evaluation.
0 kommentar(er)
