<jupyter_start><jupyter_text>Visualizing Data :<jupyter_code>def visualize_data():
    
    df = pd.read_csv('Data/nifty50_joined_closes.csv')
    df_corr = df.corr()
    
    data = df_corr.values 
    fig = plt.figure(figsize = (20, 20))
    ax = fig.add_subplot(1, 1, 1)
    
    heatmap = ax.pcolor(data, cmap = plt.cm.RdYlGn)
    fig.colorbar(heatmap)
    ax.set_xticks(np.arange(data.shape[0]) + 0.5, minor = False)
    ax.set_yticks(np.arange(data.shape[1]) + 0.5, minor = False)
    ax.invert_yaxis()
    ax.xaxis.tick_top()
    
    column_labels = df_corr.columns
    row_labels = df_corr.index
    
    ax.set_xticklabels(column_labels)
    ax.set_yticklabels(row_labels)
    
    plt.xticks(rotation = 90)
    heatmap.set_clim(-1, 1)
    plt.tight_layout()
    plt.show()
    
    fig.savefig('Plots/Nifty50_Closes_Heatmap.png')
visualize_data()<jupyter_output><empty_output><jupyter_text>Buy Sell Hold Prediction<jupyter_code># Compiling Data 
from preprocessing import * 
do_ml('RELIANCE.NS')<jupyter_output>Data spread :  Counter({'1': 1881, '-1': 1692, '0': 969})
Accuracy :  0.4
Predicted spread:  Counter({-1: 631, 1: 443, 0: 46})
{'fraction_non_alphanumeric': 0.14198606271777003, 'fraction_numerical': 0.041811846689895474, 'mean_word_length': 3.596, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '155631', 'license': 'MIT', 'n_tokens_mistral': 519, 'n_tokens_neox': 475, 'n_words': 73, 'path': 'Buy_Sell_Hold_Prediction.ipynb', 'repo_name': 'MCParab/StockMarketPrice_RNN', 'size': '1148'}