
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
import warnings
warnings.filterwarnings('ignore')
sns.set_style('darkgrid')


dataset = pd.read_csv('ICICI_BANK.csv')


dataset.shape

(5306, 15)


from IPython.display import display, HTML

# Generate HTML code for the table
table_html = """
<div style="background-color: #f9f9f9; padding: 10px; border-radius: 5px;">
    <h3>Dataset Columns</h3>
    <table>
        <tr>
            <th>Column Name</th>
        </tr>
"""

for column in dataset.columns:
    table_html += f"""
        <tr>
            <td>{column}</td>
        </tr>
    """

table_html += """
    </table>
</div>
"""

# Display the HTML table
display(HTML(table_html))


from IPython.display import display, HTML

# Generate HTML code for the table
table_html = """
<div style="background-color: #f9f9f9; padding: 10px; border-radius: 5px;">
    <h3>Dataset Info</h3>
    <table>
        <tr>
            <th>Column Name</th>
            <th>Non-Null Count</th>
            <th>Dtype</th>
        </tr>
"""

for column in dataset.columns:
    non_null_count = dataset[column].count()
    dtype = dataset[column].dtype
    table_html += f"""
        <tr>
            <td>{column}</td>
            <td>{non_null_count}</td>
            <td>{dtype}</td>
        </tr>
    """

table_html += """
    </table>
</div>
"""

# Display the HTML table
display(HTML(table_html))


from IPython.display import display, HTML

# Get the describe() output
desc = dataset.describe()

# Convert the describe() output to an HTML table
table_html = "<table>"
table_html += "<tr><th></th>"
for column in desc.columns:
    table_html += f"<th>{column}</th>"
table_html += "</tr>"

for index, row in desc.iterrows():
    table_html += "<tr>"
    table_html += f"<td>{index}</td>"
    for value in row:
        table_html += f"<td>{value:.2f}</td>"
    table_html += "</tr>"

table_html += "</table>"

# Display the HTML table
display(HTML(table_html))


# we can visualize, Before cleaning 
# display(dataset.head().style.hide_index())


# Delete unnecessary columns

# dataset.drop(["Symbol", "Series", "Prev Close", "High", "Low", "Last", "VWAP", "Volume", "Turnover", "Trades", "Deliverable Volume", "%Deliverble"], 
# axis = 1, inplace = True)
dataset.drop(columns=dataset.columns.difference(['Date', 'Open', 'Close']), inplace=True)


# we can visualize, Before cleaning 
# display(dataset.head().style.hide_index())


import plotly.express as px

fig = px.scatter(dataset.head(100), x="Open", y="Close", title="Open v/s Close", color_discrete_sequence=['orange'])
fig.show()


import plotly.graph_objects as go

# Create scatter plot
fig1 = px.scatter(dataset.head(100), x="Open", y="Close", title="Open v/s Close", color_discrete_sequence=['orange'])

# Create histogram
fig2 = px.histogram(dataset, x="Close", nbins=50, title="Histogram of Close Prices", color_discrete_sequence=['orange'])

# Create subplots
fig = go.Figure()
fig.add_trace(fig1['data'][0])
fig.add_trace(fig2['data'][0])

# Update layout
fig.update_layout(title="Scatter Plot and Histogram", showlegend=False)

# Show the figure
fig.show()


from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor


X = dataset['Open'].values
y = dataset['Close'].values


X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, test_size=0.3)


model1 = LinearRegression()
build1 = model1.fit(X_train.reshape(-1, 1), y_train)
predict1 = model1.predict(X_test.reshape(-1, 1))


print("Co-efficient: ", model1.coef_)
print("\nIntercept: ", model1.intercept_)

Co-efficient:  [0.99860402]

Intercept:  0.24768269983928803


from IPython.display import HTML

html_table = df1.head(10).to_html(index=False, justify='center', classes='table table-striped table-hover table-bordered')
styled_table = f'<div style="text-align: center;"><style>table {{border-collapse: collapse; width: 50%;}} th, td {{border: 1px solid #dddddd; text-align: left; padding: 8px;}} th {{background-color: #f2f2f2;}}</style>{html_table}</div>'
display(HTML(styled_table))


import plotly.express as px
import plotly.io as pio

# Create the bar plot
fig = px.bar(df1.head(50), title='Simple Linear Regression', barmode='group', color_discrete_sequence=px.colors.qualitative.Plotly)

# Customize the layout
fig.update_layout(
    xaxis_title='Index',
    yaxis_title='Values',
    legend_title='Data',
    width=1200,
    height=600,
    xaxis_tickangle=-45,  # Rotate x-axis labels for better readability
    showlegend=True,      # Show legend
    font=dict(size=12),   # Set font size
    plot_bgcolor='rgba(0,0,0,0)',  # Set plot background color
    paper_bgcolor='rgba(0,0,0,0)', # Set paper background color
    bargap=0.1,           # Set gap between bars
    xaxis=dict(showgrid=True, gridcolor='rgba(0,0,0,0.1)'), # Show x-axis gridlines
    yaxis=dict(showgrid=True, gridcolor='rgba(0,0,0,0.1)')  # Show y-axis gridlines
)

# Add data labels to the bars
fig.update_traces(texttemplate='%{y}', textposition='outside')

# Display the plot
fig.show()

# Save the plot as a PNG file
# pio.write_image(fig, 'bar_plot.png')


accuracy1 = r2_score(y_test, predict1)
print("Accuracy of Simple Linear Regression:", accuracy1)

Accuracy of Simple Linear Regression: 0.99849322863788


model2 = SVR(kernel="rbf", gamma = 0.01, C=100)
build2 = model2.fit(X_train.reshape(-1, 1), y_train)
predict2 = model2.predict(X_test.reshape(-1, 1))


df2 = pd.DataFrame(list(zip(y_test, predict2)), columns=["Actual Values", "Predicted Values"])


import pandas as pd

# Assuming df2 is your DataFrame
data = {'Actual Values': df2['Actual Values'], 'Predicted Values': df2['Predicted Values']}
df_table = pd.DataFrame(data)

styled_df_table = df_table.head(10).style.set_table_styles([
    {'selector': 'th.row_heading', 'props': 'display: none;'}
]).set_properties(**{'text-align': 'center'})

styled_df_table


import plotly.express as px

# Create the bar plot
fig = px.bar(df2.head(50), title='Support Vector Regression', barmode='group', color_discrete_sequence=px.colors.qualitative.Plotly)

# Customize the layout
fig.update_layout(
    xaxis_title='Index',
    yaxis_title='Values',
    legend_title='Data',
    width=1200,
    height=600,
    xaxis_tickangle=-45,  # Rotate x-axis labels for better readability
    showlegend=True,      # Show legend
    font=dict(size=12),   # Set font size
    plot_bgcolor='rgba(0,0,0,0)',  # Set plot background color
    paper_bgcolor='rgba(0,0,0,0)', # Set paper background color
    bargap=0.1,           # Set gap between bars
    xaxis=dict(showgrid=True, gridcolor='rgba(0,0,0,0.1)'), # Show x-axis gridlines
    yaxis=dict(showgrid=True, gridcolor='rgba(0,0,0,0.1)')  # Show y-axis gridlines
)

# Add data labels to the bars
fig.update_traces(texttemplate='%{y}', textposition='outside')

# Display the plot
fig.show()


accuracy2 = r2_score(y_test, predict2)
print("Accuracy of Support Vector Regression:", accuracy2)

Accuracy of Support Vector Regression: 0.9799364177634605


model3 = DecisionTreeRegressor()
build3 = model3.fit(X_train.reshape(-1, 1), y_train)
predict3 = model3.predict(X_test.reshape(-1, 1))


df3 = pd.DataFrame(list(zip(y_test, predict3)), columns=["Actual Values", "Predicted Values"])


import pandas as pd

# Assuming df3 is your DataFrame
data = {'Actual Values': df3['Actual Values'], 'Predicted Values': df3['Predicted Values']}
df_table = pd.DataFrame(data)

styled_df_table = df_table.head(10).style.set_table_styles([
    {'selector': 'th.row_heading', 'props': 'display: none;'}
]).set_properties(**{'text-align': 'center'})

styled_df_table


import plotly.express as px

# Create the bar plot
fig = px.bar(df3.head(50), title='Decision Tree Regression', barmode='group', color_discrete_sequence=px.colors.qualitative.Plotly)

# Customize the layout
fig.update_layout(
    xaxis_title='Index',
    yaxis_title='Values',
    legend_title='Data',
    width=1200,
    height=600,
    xaxis_tickangle=-45,  # Rotate x-axis labels for better readability
    showlegend=True,      # Show legend
    font=dict(size=12),   # Set font size
    plot_bgcolor='rgba(0,0,0,0)',  # Set plot background color
    paper_bgcolor='rgba(0,0,0,0)', # Set paper background color
    bargap=0.1,           # Set gap between bars
    xaxis=dict(showgrid=True, gridcolor='rgba(0,0,0,0.1)'), # Show x-axis gridlines
    yaxis=dict(showgrid=True, gridcolor='rgba(0,0,0,0.1)')  # Show y-axis gridlines
)

# Add data labels to the bars
fig.update_traces(texttemplate='%{y}', textposition='outside')

# Display the plot
fig.show()


accuracy3 = r2_score(y_test, predict3)
print("Accuracy of Decision Tree Regression:", accuracy3)

Accuracy of Decision Tree Regression: 0.9973466715973613


model4 = RandomForestRegressor(n_estimators=100)
build4 = model4.fit(X_train.reshape(-1, 1), y_train)
predict4 = model4.predict(X_test.reshape(-1, 1))


df4 = pd.DataFrame(list(zip(y_test, predict4)), columns=["Actual Values", "Predicted Values"])


import pandas as pd

# Assuming df4 is your DataFrame
data = {'Actual Values': df4['Actual Values'], 'Predicted Values': df4['Predicted Values']}
df_table = pd.DataFrame(data)

styled_df_table = df_table.head(10).style.set_table_styles([
    {'selector': 'th.row_heading', 'props': 'display: none;'}
]).set_properties(**{'text-align': 'center'})

styled_df_table


import plotly.express as px

# Create the bar plot
fig = px.bar(df4.head(50), title='Random Forest Regression', barmode='group', color_discrete_sequence=px.colors.qualitative.Plotly)

# Customize the layout
fig.update_layout(
    xaxis_title='Index',
    yaxis_title='Values',
    legend_title='Data',
    width=1200,
    height=600,
    xaxis_tickangle=-45,  # Rotate x-axis labels for better readability
    showlegend=True,      # Show legend
    font=dict(size=12),   # Set font size
    plot_bgcolor='rgba(0,0,0,0)',  # Set plot background color
    paper_bgcolor='rgba(0,0,0,0)', # Set paper background color
    bargap=0.1,           # Set gap between bars
    xaxis=dict(showgrid=True, gridcolor='rgba(0,0,0,0.1)'), # Show x-axis gridlines
    yaxis=dict(showgrid=True, gridcolor='rgba(0,0,0,0.1)')  # Show y-axis gridlines
)

# Add data labels to the bars
fig.update_traces(texttemplate='%{y}', textposition='outside')

# Display the plot
fig.show()


accuracy4 = r2_score(y_test, predict4)
print("Accuracy of Random Forest Regression:", accuracy4)

Accuracy of Random Forest Regression: 0.9979437067080489


dict1 = {
    "Model": ["Simple Linear Regression", "Support Vector Regression", "Decision Tree Regression", "Random Forest Regression"],
    "Accuracy": np.array([accuracy1, accuracy2, accuracy3, accuracy4])
}
df = pd.DataFrame(dict1)
styled_df = df.style.set_table_styles([{'selector': 'tr:hover','props': [('background-color', 'yellow')]}])
styled_df


import plotly.graph_objects as go

models = ['SLR', 'SVR', 'DTR', 'RFR']
acc = [accuracy1, accuracy2, accuracy3, accuracy4]

# Create a figure
fig = go.Figure()

# Add bar trace
fig.add_trace(go.Bar(
    x=models,
    y=acc,
    marker=dict(color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']),  # Change bar colors
    text=acc,
    textposition='auto'
))

# Update layout
fig.update_layout(
    title='Comparison of Accuracies of Models',
    yaxis=dict(title='Accuracy'),
    xaxis=dict(title='Models'),
    yaxis_tickformat='.2%',  # Format y-axis ticks as percentage
    height=500,  # Set plot height
)

# Show plot
fig.show()


html_table = future_stock_value.to_html(index=False, justify='center', classes='table table-striped table-hover table-bordered')
styled_table = f'<style>table {{border-collapse: collapse; width: 50%;}} th, td {{border: 1px solid #dddddd; text-align: left; padding: 8px;}} th {{background-color: #f2f2f2;}}</style>{html_table}'
display(HTML(styled_table))


models = np.array(df['Model'])
accuracy = np.array(df['Accuracy'])


highest_accuracy=0.0
best_model=""


for i in range(len(accuracy)) :
    if accuracy[i] >= highest_accuracy :
        highest_accuracy=accuracy[i]
        best_model=models[i]


slr, svr, dtr, rfr = [], [], [], []

if best_model == models[0] :
    future_stock_value['Predicted'] = model1.predict(future_stock_value.Open.values.reshape(-1, 1))
elif best_model == models[1] :
    future_stock_value['Predicted'] = model2.predict(future_stock_value.Open.values.reshape(-1, 1))
elif best_model == models[2] :
    future_stock_value['Predicted'] = model3.predict(future_stock_value.Open.values.reshape(-1, 1))
elif best_model == models[3] :
    future_stock_value['Predicted'] = model4.predict(future_stock_value.Open.values.reshape(-1, 1))


print(future_stock_value.to_string(index=False))

     Date  Open  Predicted
11-May-22 718.0  717.24537


fig, ax = plt.subplots()
ax.axis('off')

props = dict(boxstyle='round', facecolor='lightblue', alpha=0.5)
ax.text(0.5, 0.5, 'THANK YOU', va='center', ha='center', fontsize=30, fontweight='bold', bbox=props)

plt.show()

	Prev Close	Open	High	Low	Last	Close	VWAP	Volume	Turnover	Trades	Deliverable Volume	%Deliverble
count	5306.00	5306.00	5306.00	5306.00	5306.00	5306.00	5306.00	5306.00	5306.00	2456.00	4789.00	4789.00
mean	550.90	551.56	560.56	541.53	551.05	551.00	551.13	8224630.71	375929920576296.62	138367.63	4183406.04	0.47
std	368.78	368.89	374.08	363.39	368.71	368.73	368.75	12185349.04	475813345498016.69	99008.73	6365381.73	0.13
min	67.40	67.00	70.45	66.00	67.00	67.40	68.52	7409.00	96172830000.00	2595.00	15015.00	0.10
25%	267.56	267.40	271.91	263.62	267.40	267.61	267.58	961205.50	34594425000000.00	79312.25	699502.00	0.38
50%	398.08	399.00	406.52	392.45	398.70	398.18	398.24	3486647.50	292301000000000.00	110101.00	1963117.00	0.48
75%	873.56	877.00	888.77	859.80	874.60	873.56	873.51	11572021.25	499352750000000.00	162953.50	5948817.00	0.56
max	1794.10	1767.05	1798.15	1760.15	1793.00	1794.10	1783.46	286857658.00	14600000000000000.00	949891.00	232530747.00	0.98

Actual Values	Predicted Values
136.80	137.635962
330.20	339.076123
879.15	872.417691
262.50	263.598544
580.70	569.507672
1040.20	1024.272582
360.70	359.570035
852.15	856.422442
829.05	869.418582
187.60	184.622005

	Actual Values	Predicted Values
0	1023.250000	1016.899042
1	392.050000	392.051720
2	550.900000	533.321320
3	301.100000	302.149611
4	362.300000	352.221443
5	212.900000	214.536149
6	264.000000	263.939771
7	631.750000	608.350294
8	967.900000	966.007576
9	574.700000	545.773854

	Actual Values	Predicted Values
0	1023.250000	1022.950000
1	392.050000	383.900000
2	550.900000	528.700000
3	301.100000	303.883333
4	362.300000	348.425000
5	212.900000	211.000000
6	264.000000	264.450000
7	631.750000	601.550000
8	967.900000	966.050000
9	574.700000	549.300000

	Actual Values	Predicted Values
0	1023.250000	1019.395500
1	392.050000	392.366250
2	550.900000	530.443500
3	301.100000	303.901994
4	362.300000	348.924955
5	212.900000	211.364093
6	264.000000	264.753700
7	631.750000	603.482300
8	967.900000	968.403500
9	574.700000	550.432000

Data Import and Cleaning¶

Data Visualization¶

Model Building¶

Model Evaluation¶

Results Visualization¶

Predicting Future Stock Price¶

Data Import and Cleaning:¶

Import Libraries and Dataset¶

Information about the dataset¶

Dataset Columns

Dataset Info

Data Cleaning¶

Data Visualization:¶

Import Models¶

Model Building:¶

Simple Linear Regression¶

Support Vector Regression¶

Decision Tree Regression¶

Random Forest Regression¶

Results Visualization:¶

Find out the closing price of the company of that day¶

Predicting Future Stock Price:¶

OUTPUT IMAGES

Column Name	Non-Null Count	Dtype
Date	5306	object
Symbol	5306	object
Series	5306	object
Prev Close	5306	float64
Open	5306	float64
High	5306	float64
Low	5306	float64
Last	5306	float64
Close	5306	float64
VWAP	5306	float64
Volume	5306	int64
Turnover	5306	float64
Trades	2456	float64
Deliverable Volume	4789	float64
%Deliverble	4789	float64

	Model	Accuracy
0	Simple Linear Regression	0.998493
1	Support Vector Regression	0.979936
2	Decision Tree Regression	0.997347
3	Random Forest Regression	0.997944