!pip install mplfinance

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from mplfinance.original_flavor import candlestick_ohlc
import matplotlib.dates as mdates

data = pd.read_csv('/content/berkshire_hathaway_data.csv')

data.isnull().sum()
data = data.dropna()
data.dtypes

data['Date'] = pd.to_datetime(data['Date'])

plt.figure(figsize=(10, 6))
plt.plot(data['Date'], data['Close'], label='Close Price')
plt.xlabel('Date')
plt.ylabel('Close Price')
plt.title('Close Price Over Time')
plt.legend()
plt.grid(True)
plt.show()

ohlc = data[['Date', 'Open', 'High', 'Low', 'Close']]
ohlc['Date'] = ohlc['Date'].map(mdates.date2num)
fig, ax = plt.subplots(figsize=(10, 6))
candlestick_ohlc(ax, ohlc.values, width=0.6, colorup='g', colordown='r')
ax.xaxis_date()
ax.set_xlabel('Date')
ax.set_ylabel('Price')
ax.set_title('Candlestick Chart')
plt.grid(True)
plt.show()

plt.figure(figsize=(10, 6))
plt.bar(data['Date'], data['Volume'], label='Volume')
plt.xlabel('Date')
plt.ylabel('Volume')
plt.title('Volume Over Time')
plt.legend()
plt.grid(True)
plt.show()

data['MA50'] = data['Close'].rolling(window=50).mean()
plt.figure(figsize=(10, 6))
plt.plot(data['Date'], data['Close'], label='Close Price')
plt.plot(data['Date'], data['MA50'], label='50-Day MA')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Close Price and 50-Day Moving Average')
plt.legend()
plt.grid(True)
plt.show()

plt.figure(figsize=(10, 6))
plt.scatter(data['High'], data['Low'], alpha=0.5, label='High vs. Low')
plt.xlabel('High Price')
plt.ylabel('Low Price')
plt.title('High vs. Low Price')
plt.legend()
plt.grid(True)
plt.show()

plt.figure(figsize=(10, 6))
plt.scatter(data['Open'], data['Close'], alpha=0.5, label='Open vs. Close')
plt.xlabel('Open Price')
plt.ylabel('Close Price')
plt.title('Open vs. Close Price')
plt.legend()
plt.grid(True)
plt.show()

plt.figure(figsize=(10, 6))
sns.heatmap(data.corr(), annot=True, cmap='coolwarm', vmin=-1, vmax=1)
plt.title('Correlation Heatmap')
plt.show()