Case 1

from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd

def knncls():

    """
    //Forecast movie classification
    :return:
    """
    data = pd.read_csv("./data/movies.csv")
    # Extract eigenvalue, target value
    x = data.drop(["type", "movie_name"], axis=1)
    y = data["type"]
    # Split data set
    x_train, x_test, y_train, y_test =train_test_split(x, y, test_size=0.25)

    # Forecast by knn
    knn = KNeighborsClassifier()

    knn.fit(x_train, y_train)

    y_predict = knn.predict(x_test)
    print(x_test, "The forecast result of is:", y_predict)

    print("The prediction accuracy is:", knn.score(x_test, y_test))

if __name__ == '__main__':
    knncls()

Accuracy

movie_name,fight,kiss,type
California Man,3,104,1
He's not Really into dues,2,100,1
Beautiful Woman,1,81,1
Kevin Longblade,101,10,2
Robo Slayer 3000,99,5,2
Amped II,98,2,2
unname,18,90,1
vampire,90,15,2

Case 2 Facebook check in location

facebook predicts where to stay

train_data

from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd

def knncls():
    """
    facebook subject:k Nearest neighbor algorithm predicts occupancy location
    :return:
    """
    # Using pandas to read 100000 data
    train_data = pd.read_csv("./data/fb/train.csv", nrows = 100000)

    # Characteristic Engineering
    # 1. Reduce the range of x,y
    train_data = train_data.query("x>1.0 & x<1.5 & y>1.0 & y<2.5")

    # 2. Parse timestamp
    time_value = pd.to_datetime(train_data["time"], unit="s")
    time_value = pd.DatetimeIndex(time_value)

    # 3. Add feature (time)
    train_data["weekday"] = time_value.weekday
    train_data["year"] = time_value.day
    train_data["hour"] = time_value.hour
    train_data["minute"] = time_value.minute

    # 4. Delete feature (time stamp)
    train_data = train_data.drop(["time"], axis=1)

    # 5. Only the place s with more than 5 check-in persons are reserved to generate new train_data
    place_count = train_data.groupby("place_id").count()
    place_count_r = place_count[place_count.row_id > 3].reset_index()
    train_data = train_data[train_data["place_id"].isin(place_count_r["place_id"])]

    # Extract eigenvalues and target values
    x = train_data.drop(["place_id", "row_id"], axis=1)

    y = train_data["place_id"]

    # Split data set
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)

    # Standardize
    std = StandardScaler()

    x_train = std.fit_transform(x_train)
    x_test = std.transform(x_test)

    # Instantiate knn estimator
    knn = KNeighborsClassifier()

    knn.fit(x_train, y_train)

    # Forecast results
    y_predict = knn.predict(x_test)

    # Printing accuracy
    print("The accuracy is:",knn.score(x_test, y_test))

    return None

if __name__ == '__main__':
    knncls()