docs: update readme Usage section and fix grammar

README.md

@@ -8,12 +8,12 @@ Kelly Wu, Morris Zhao, Spencer Gerlach, Ty Andrews
 
 ## Python ecosystem
 
-Our package is unique, it provides an easy way to investigate used Mercedes Benz car prices. It provide people a big picture about the market. The package is rely on the real market data set to plot, filter and predict. It also gives advice to buyers and seller wehn they try to make a decision.
+Our package is unique, it provides an easy way to investigate used Mercedes Benz car prices. It provide people a big picture about the market. The package relies on a real market data set to plot, filter and predict. It also gives advice to buyers and sellers wehn they try to make a decision.
 
 ## Functions
 
 The package contains the following functions:
-1. `load_sample_mercedes_listings`: Retrieves a data frame that containing sample data of used Mercedez Benz vehicles.
+1. `load_sample_mercedes_listings`: Retrieves a data frame that contains sample data of used Mercedez Benz vehicles.
 2. `plot_mercedes_price`: Plot a density plot of a Mercedes-Benz model to see where the current vehicle's price falls for that same model in the market.
 3. `listing_search`: Retrieves the top listings that are within the budget range specified by the user.
 4. `predict_mercedes_price`: Predicts the price in USD of a Mercedes-Benz given the year, model, condition, and number of cylinders.
@@ -30,7 +30,26 @@ $ pip install mercedestrenz
 
 ## Usage
 
-- TODO
+Below is a basic example of how to use each of the four functions included in this package.
+
+```
+# Load all required package functions
+from mercedestrenz.data import load_sample_mercedes_listings, listing_search
+from mercedestrenz.modelling import train_mercedes_price_prediction_model
+from mercedestrenz.modelling import predict_mercedes_price
+
+# Load the sample mercedes listings data into a dataframe
+data = load_sample_mercedes_listings()
+
+# Return the top listings that are within a budget range specified by the user. Returns a pandas dataframe of results.
+listing_search(data, budget=[2000, 20000], model = "any", sort_feature = "odometer_mi", ascending = True)
+
+# Plot a price distribution of specific mercedes models, and see where an input price falls in the distribution.
+plot_mercedes_price(model='s-class', price=80000, market_df=data))
+
+# Predict the price (in USD) of a Mercedes-Benz given the year, model, condition, paint color, and odometer reading.
+predict_mercedes_price("e-class", 2015, 55_000, "fair", "silver")
+```
 
 ## Contributing