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sns.distplot(full_df['Price'])

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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")

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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")

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sns.heatmap(df.corr())

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn.decomposition import PCA
from sklearn.neighbors import KNeighborsRegressor

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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

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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