vivekpadia70

knr = KNeighborsRegressor(weights='distance', n_neighbors=200)
knr.fit(X_train, Y_train)
print("KNN Score: ", knr.score(X_train, Y_train))
print("KNN Test Score: ", knr.score(X_test, Y_test))

vivekpadia70

sns.distplot(full_df['Price'])

vivekpadia70

plt.figure(figsize=(10,7), facecolor='w', edgecolor='k')
sns.violinplot(full_df['Regionname'], full_df['Price'])
plt.title("Violin plot for Regionname to Price")
plt.xticks(rotation=45)

vivekpadia70

plt.figure(figsize=(10,7), facecolor='w', edgecolor='k')
sns.violinplot(full_df['Type'], full_df['Price'])
plt.title("Violin plot for Type to Price")

vivekpadia70

plt.figure(figsize=(10,7), facecolor='w', edgecolor='k')
sns.scatterplot(full_df["Longtitude"], full_df["Lattitude"], hue=full_df["Price"], size=full_df["Landsize"])
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.2)
plt.title("Lattitude to Longitude with Price and Landsize")

vivekpadia70

plt.figure(figsize=(10,7), facecolor='w', edgecolor='k')
sns.scatterplot(full_df["Longtitude"], full_df["Lattitude"], hue=full_df["Type"])
plt.title("Lattitude to Longitude for Type of room")

vivekpadia70

plt.figure(figsize=(10,7), facecolor='w', edgecolor='k')
sns.scatterplot(full_df["Longtitude"], full_df["Lattitude"], hue=full_df["Regionname"])
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.2)
plt.title("Lattitude to Longitude for Regionname")

vivekpadia70

sns.heatmap(df.corr())

vivekpadia70

import numpy as np
import gym

from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.optimizers import Adam

from rl.agents.dqn import DQNAgent
from rl.policy import EpsGreedyQPolicy
from rl.memory import SequentialMemory

vivekpadia70

ENV_NAME = 'Breakout-ram-v0'

# Get the environment
env = gym.make(ENV_NAME)
np.random.seed(33)
env.seed(33)

# Extract the number of actions available in Breakout, i.e. left and right
nb_actions = env.action_space.n